# 360Anything: Geometry-Free Lifting of Images and Videos to 360◦

Ziyi Wu1,3, Daniel Watson1, Andrea Tagliasacchi2,3,†, David J. Fleet1,3, Marcus A. Brubaker1, and Saurabh Saxena1

1 Google DeepMind 2 Simon Fraser University 3 University of Toronto

Abstract. Lifting perspective images and videos to 360◦ panoramas enables immersive 3D world generation. Existing approaches often rely on explicit geometric alignment between the perspective and the equirectangular projection (ERP) space. Yet, this requires known camera metadata, obscuring the application to in-the-wild data where such calibration is typically absent or noisy. We propose 360Anything, a geometry-free framework built upon pre-trained diffusion transformers. By treating the perspective input and the panorama target simply as token sequences, 360Anything learns the perspective-to-equirectangular mapping in a purely data-driven way, eliminating the need for camera information. Our approach achieves state-of-the-art performance on both image and video perspective-to-360◦ generation, outperforming prior works that use ground-truth camera information. We also trace the root cause of the seam artifacts at ERP boundaries to zero-padding in the VAE encoder, and introduce Circular Latent Encoding to facilitate seamless generation. Finally, we show competitive results in zero-shot camera FoV and orientation estimation benchmarks, demonstrating 360Anything’s deep geometric understanding and broader utility in computer vision tasks. Additional results are available at https://360anything.github.io.

Keywords: Panorama generation · Diffusion transformer · Outpainting

# 1 Introduction

Generating photorealistic 3D worlds is an exciting and challenging frontier in generative modeling, offering transformative potential across robotics, AR/VR, and gaming. Recent years have witnessed significant advancements in this domain [36,66,75,81], largely propelled by the dramatic progress in visual generative models [5, 34, 60, 67, 79]. However, standard generators produce perspective views, capturing a narrow view of the physical world, and limiting their utility in creating fully immersive 3D worlds. This limitation has spurred significant interest in 360◦ generative models [71, 82, 84, 100], especially those for lifting perspective imagery to omnidirectional 360◦ panoramas [14, 29, 46, 74].

![](images/7e52e06ea95cc14912b044e0ad6245b08bc273fde5135ad4f4d411f5bc7ea9d0.jpg)  
Fig. 1: 360Anything lifts arbitrary perspective images (row 1) and videos (row 2) to seamless, gravity-aligned $3 6 0 ^ { \circ }$ panoramas. Model inputs and their projected regions are highlighted in red or green. Below each panorama, we show four perspective projections facing left, front, right, and back. Without using explicit camera information, 360Anything handles images with varying Field-of-View and videos with large object and camera motion. The generated consistent panoramas enable 3D scene reconstruction via 3D Gaussian Splatting (row 3). Please see our project page for results in 360◦ viewers.

Despite recent progress, current approaches for perspective-to-panorama generation lack the robustness needed for “in-the-wild” inputs. To bridge the gap between perspective and panoramic spaces, prior works often rely on strong geometric inductive biases, such as explicitly projecting the perspective input to the target Equirectangular Projection (ERP) space to provide an aligned conditioning signal [46, 74, 89, 102]. However, this strategy requires known camera metadata, such as Field-of-View (FoV) and camera pose (yaw, pitch, roll) [46]. As a consequence, these models struggle with inputs where such metadata is absent, or are brittle when relying on noisy external estimators.

We posit that explicit geometric alignment is unnecessary for panorama generation. Instead, with sufficient data, a general-purpose architecture should be able to learn these relationships from data. To this end, our proposed framework utilizes a diffusion transformer (DiT) [54], and treats the perspective input and target panorama simply as token sequences. With attention on the concatenated sequence, the model learns their geometric relationship. This enables the model to effectively “place” the perspective input onto the $3 6 0 ^ { \circ }$ canvas and synthesize the remaining context, handling varying FoVs and camera poses as shown in Figure 1. Our pipeline thus eliminates the camera estimation step and makes the task fully end-to-end, which enjoys the benefit of scaling up model and data.

Beyond an end-to-end framework, we also address the common issue of seam artifacts at the boundary of a generated ERP image. Existing works rely on inference time tricks such as rotation augmentations to mitigate visible seams [46, 84, 89]. In contrast, we identify and eliminate the root cause of these artifacts during the training stage itself. Modern diffusion models often operate in the latent space of a convolution-based VAE [60], and VAEs utilize zeropadding in convolutional layers [27]. This introduces boundary artifacts in the latent representation of panorama data, which leads to seams in the generated panoramas. We propose a simple solution that uses circular padding when encoding VAE latents. This ensures that the latent representation has circular continuity, thereby eliminating the root cause of seam artifacts.

In summary, this work makes the following contributions:

1. We propose 360Anything, a novel DiT-based architecture for “in-the-wild” perspective to canonical panorama generation that implicitly infers camera intrinsics and extrinsics, eliminating the need for camera calibration.   
2. We identify VAE latent encoding as the root cause of seam artifacts in panorama generation and propose a simple remedy that mitigates the issue.   
3. Despite not using camera metadata, 360Anything achieves state-of-the-art performance for panoramic image and video generation, outperforming baselines that have access to extra camera information.   
4. We evaluate the accuracy of our estimated FoV and camera poses, showing competitive results against supervised baselines. Furthermore, we can reconstruct consistent 3D scenes from our generated panoramic videos.

# 2 Related Work

Panorama Image Generation. Early approaches used GANs [10, 17, 19, 45, 48, 49, 69] or autoregressive generators [1, 8, 12, 104]. Recent methods have switched to diffusion models [22, 60] due to their state-of-the-art performance. One line of work generates panoramas from text prompts [14, 50, 71, 82]. They often design better panorama representations [6, 51] or panorama-aware operations [95, 99] to reuse knowledge in pre-trained perspective generators. Closer to 360Anything are methods that outpaint panoramas from narrow field-of-view images [37,42,80,94]. The majority of them project the conditioning perspective image to ERP space to be pixel-aligned with the target panorama, and then execute diffusion in ERP space [83, 89]. To handle the geometric properties of panoramas, they often inject strong inductive bias such as spherical convolutions [73, 88] and dual branch architectures [102]. A few works explore the cubemap representation to eliminate large distortions inherent in the ERP [25, 29]. However, existing methods either require known camera information to perform the projection, or assume the conditioning image has a fixed viewpoint and FoV. In contrast, our method treats the problem as a sequence-to-sequence task learned directly from data.

Panorama Video Generation. Some works directly fine-tune pre-trained video generators to produce panorama videos from text [43, 84, 90, 100]. This paper instead tackles the task of panoramic outpainting from perspective videos [40, 47, 93]. Imagine360 [74] duplicates the denoising U-Net in AnimateDiff [18] to process panorama and perspective views separately, connected by spherical attention for information exchange. ViewPoint [13] proposes an improved cubemap representation to reduce the geometric distortion of ERP. Argus [46] further scales up training data to unconstrained YouTube videos [78]. Nevertheless, these methods (i) rely on external tools to estimate camera metadata of the conditioning video, and (ii) use inference tricks to eliminate seams in the generation. In contrast, 360Anything learns to pose the input video in 4D space, and remove seams by identifying its root cause and adjusting the architecture.

Prior-Free Learning with Transformers. Recently, Transformers have dominated tasks that previously relied on inductive bias, including image generation [54,92], editing [35,87], and 3D understanding [61,86]. While the majority of panorama generation methods still leverage a U-Net based architecture [29, 46], we identify this as a major limiting factor for the field. 360Anything instead runs Transformers on a sequence of tokens without any geometric prior, while achieving state-of-the-art results across multiple tasks.

# 3 Method

Task formulation. Given a perspective video with T frames, $\mathrm { X } _ { \mathrm { p e r s } } \in \mathbb { R } ^ { T \times h \times w \times 3 }$ (we treat image as a special case with T =1) and a caption e, our goal is to outpaint a $3 6 0 ^ { \circ }$ panoramic video $\mathrm { Y } _ { \mathrm { e q u i } } \in \mathbb { R } ^ { T \times \tilde { H } \times W \times 3 }$ . In this work, we represent panorama data in the Equirectangular Projection (ERP) space.

Overview. Our method builds upon pre-trained latent diffusion transformers [54] (Sec. 3.1). We leverage a simple sequence concatenation approach to learn the perspective-to-equirectangular mapping and generate panoramas in a gravityaligned canonical space (Sec. 3.2). Finally, we address the seam artifacts of generated panoramas by analyzing the panoramic latent space (Sec. 3.3). The overall architecture of 360Anything is shown in Figure 2.

# 3.1 Background

We adopt the flow matching framework [41, 44], which learns a denoiser $\mathcal { G } _ { \theta }$ that maps from the standard normal distribution $\epsilon \sim \mathcal { N } ( 0 , I )$ ) to the distribution of panorama data $\mathrm { Y _ { e q u i } } \sim p _ { \mathrm { d a t a } }$ . The forward diffusion process adds noise to clean data to obtain a noisy input $\mathrm { Y _ { e q u i } ^ { t } }$ at time $t \in [ 0 , 1 ]$ ; i.e., $\mathrm { Y _ { e q u i } ^ { t } } = ( 1 - t ) \mathrm { Y _ { e q u i } } + t \epsilon$ The denoiser $\mathcal { G } _ { \theta }$ , implemented as a neural network parameterized by $\theta ,$ , is trained to reverse this process with the following objective:

$$
\min _ {\boldsymbol {\theta}} \mathbb {E} _ {t \sim p (t), \mathrm{Y} _ {\text {equi}} \sim p _ {\text {data}}, \boldsymbol {\epsilon} \sim \mathcal {N} (\boldsymbol {0}, \boldsymbol {I})} \| (\boldsymbol {\epsilon} - \mathrm{Y} _ {\text {equi}}) - \mathcal {G} _ {\boldsymbol {\theta}} \left(\mathrm{Y} _ {\text {equi}} ^ {\mathrm{t}}, t, \boldsymbol {c}\right) \| ^ {2}, \tag {1}
$$

where $p ( t )$ is the distribution of noise levels [11] and c refers to the auxiliary conditioning inputs, which in our case are the caption and the perspective input. We implement the denoiser $\mathcal { G } _ { \theta }$ as a diffusion transformer (DiT) [34, 54, 79].

![](images/19cfcf56bce9b79ced9b7c7da41ce94ae9d5b1c5a8e87320da4435e7c79d3bd1.jpg)

<details>
<summary>flowchart</summary>

```mermaid
graph LR
    A["Raw 360° video"] --> B["Canonicalized 360° video"]
    B --> C["Perspective video"]
    C --> D["Sequence concat"]
    D --> E["DiT"]
    E --> F["Denoised tokens"]
    F --> G["Generated 360° video"]

    subgraph Raw 360° video
        H["Estimate camera poses"] --> I["Project"]
        J["Estimate gravity direction"] --> K["Project"]
        L["Estimate photo"] --> M["Project"]
    end

    subgraph Canonicalized 360° video
        N["Project"] --> O["Latent encoding"]
        P["Project"] --> Q["Latent encoding & Add noise"]
    end

    subgraph Perspective video
        R["Project"] --> S["Latent encoding"]
        T["Project"] --> U["Latent encoding & Add noise"]
    end

    subgraph Sequence concat
        V["..."] --> W["DiT"]
        X["..."] --> W
        Y["..."] --> W
        Z["..."] --> W
        AA["..."] --> W
        AB["..."] --> W
        AC["..."] --> W
        AD["..."] --> W
        AE["..."] --> W
        AF["..."] --> W
        AG["..."] --> W
    end

    subgraph DiT
        AH["Caption &quot;A 360° image of ...&quot;"] --> AI["DiT"]
    end

    subgraph Denoised tokens
        AJ["..."]
        AK["..."]
        AL["..."]
        AM["..."]
        AN["..."]
        AO["..."]
        AP["..."]
        AQ["..."]
        AR["..."]
        AS["..."]
        AT["..."]
        AU["..."]
        AV["..."]
        AW["..."]
    end

    subgraph Generated 360° video
        AX["..."]
        AY["..."]
        AZ["..."]
        BA["..."]
        BB["..."]
        BC["..."]
        BD["..."]
        BE["..."]
        BF["..."]
    end

    style Raw 360° video fill:#f9f,stroke:#333
    style Canonicalized 360° video fill:#ccf,stroke:#333
    style Perspective video fill:#cfc,stroke:#333
    style Sequence concat fill:#fcc,stroke:#333
    style DiT fill:#ffc,stroke:#333
    style Denoised tokens fill:#cfc,stroke:#333
    style Generated 360° video fill:#fcc,stroke:#333
```
</details>

Fig. 2: 360Anything pipeline. Given a raw $3 6 0 ^ { \circ }$ training video with arbitrary camera orientations, we first estimate per-frame camera poses and rotate frames to align with the first frame. We then estimate the video’s gravity direction and align it with the vertical axis. With such a canonicalized $3 6 0 ^ { \circ }$ video, we project it to a perspective video using randomly sampled camera intrinsics and poses (Sec. 4.2). We then encode both the conditioning and target videos to latent tokens. Critically, we employ Circular Latent Encoding for the target $3 6 0 ^ { \circ }$ video to avoid seam artifacts in the latent representation. The conditioning tokens (orange) and noisy target tokens (green) are concatenated along the sequence dimension and fed into a diffusion transformer (DiT). The denoised tokens can be decoded back to a $3 6 0 ^ { \circ }$ video via circular latent decoding.

To generate samples at a high resolution, modern diffusion models typically operate in the latent space of a pre-trained convolution-based VAE [60]. Following this practice, we encode panorama data $\mathrm { Y _ { e q u i } }$ to a latent representation $\mathrm { { y } _ { \mathrm { { e q u i } } } }$ via an encoder E, which can be decoded back to the pixel space with a decoder D:

$$
\mathrm{y} _ {\text {equi}} = \mathcal {E} (\mathrm{Y} _ {\text {equi}}), \hat {\mathrm{Y}} _ {\text {equi}} = \mathcal {D} (\mathrm{y} _ {\text {equi}}). \tag {2}
$$

The latent representation $\operatorname { y } _ { \mathrm { { e q u i } } }$ is then patchified and flattened into a 1D sequence of tokens that is provided as input to the DiT.

# 3.2 Geometry-Free Scalable Panorama Generation

A core challenge in perspective-to-panorama generation lies in finding an effective solution for conditioning the model on the perspective input $\mathrm { X _ { p e r s } }$ . Prior works [46, 74, 83, 89] typically project $\mathrm { X _ { p e r s } }$ into the ERP space to obtain $\mathrm { X _ { e q u i } ^ { p r o j } }$ , which is pixel-aligned with the generation target $\mathrm { Y _ { e q u i } }$ . Then, they concatenate the latent of Xproj $\mathrm { X } _ { \mathrm { e q u i } } ^ { \mathrm { p r o j } }$ and the noisy latent ${ \mathrm { y } } _ { \mathrm { e q u i } } ^ { \mathrm { t } }$ channel-wise as the input to the diffusion model. This approach imposes a strong geometric inductive bias by explicitly localizing the perspective input $\mathrm { X _ { p e r s } }$ on the panorama output $\mathrm { Y _ { e q u i } }$ , drastically simplifying the task to image outpainting. However, pixel-aligned perspective-topanorama projection requires precise camera Field-of-View (FoV) and orientation estimates [46]. For in-the-wild test data, this information is generally unavailable, and existing estimation methods can be noisy [39,77,85], resulting in accumulated errors and suboptimal performance. Consequently, when off-the-shelf camera estimators fail, channel-concatenation approaches break down completely due to the reliance on pixel-aligned conditioning; see Appendix B.1.

Sequence concatenation. We propose to relax this constraint by treating geometric alignment as a task we can learn from data. Instead of enforcing spatial correspondence via projection into the ERP space, we employ a simple sequence concatenation mechanism inspired by recent image editing models [35, 87]. We directly encode the perspective input to latents: $\mathrm { x _ { p e r s } } = \mathcal { E } ( \mathrm { X _ { p e r s } } )$ , and append it to the noisy latents ${ \mathrm { y } } _ { \mathrm { e q u i } } ^ { \mathrm { t } }$ as the DiT input: Concat $\big ( \big [ \mathrm { x _ { p e r s } } , \mathrm { y _ { e q u i } ^ { t } } \big ] \big )$ . The DiT thus runs global self-attention on the combined sequence of tokens. It learns to generate latents in the ERP image by reasoning their relationship to latents in the perspective image in a purely data-driven way.

Generating canonical panoramas. Since we do not provide explicit camera pose to the model, the generated panorama $\mathrm { Y _ { e q u i } }$ can be in any coordinate system. Prior works [13, 29] assume the conditioning view $\mathrm { X _ { p e r s } }$ is always at the center of the ERP, hence generating panoramas with “unnatural” gravity directions (i.e., not pointing towards the bottom of the panorama). However, this requires the model to learn different spherical distortion patterns depending on the actual pose of the input $\mathrm { X _ { p e r s } }$ . Our ablations show that this leads to degraded visual quality (see Tab. 7). Instead, we enforce a Canonical Coordinate constraint, for which the model is trained to generate panoramas in a standard, gravity-aligned upright orientation, regardless of the camera pose of the input $\mathrm { X _ { p e r s } }$ . This requires the model to infer the camera pose of $\mathrm { X _ { p e r s } }$ to “place” it on a canonical 360◦ canvas, and generate the rest of the panorama accordingly.

Implementing the canonical training objective requires ground-truth panoramas to be consistently aligned. This condition is naturally satisfied by our image datasets as they are predominantly synthetic renderings of 3D scenes [103]. Yet, our real-world video datasets [78] frequently exhibit arbitrary non-canonical orientations. Thus, we design a two-stage data pre-processing pipeline. We first apply COLMAP [64] to estimate per-frame camera pose, and rotate each frame to have zero rotation relative to the first frame. Then, we run GeoCalib [77] to estimate the global gravity direction of the stabilized video, and rotate the video to align the gravity direction with the vertical axis. This data pre-processing step ensures the model trains on consistent, gravity-aligned data, thereby generating canonical videos at test time. See Figure 8 in the Appendix for an example.

# 3.3 Seam-free Generation via Circular Latent Encoding

A common issue in panorama generation is “seam artifacts”, where the left and right boundaries of the ERP image have visible discontinuities when concatenated (see Fig. 7). Prior works often attribute this to the generation process, employing inference time tricks such as rotated denoising (shifting the panorama cyclically across sampling steps) [89, 90] and circular padding in the VAE decoder [84].

We argue that the root cause of seams lies not in the inference stage, but in the training stage. Modern diffusion models are often applied in the latent space of a convolution-based VAE. When encoding a panorama image in the ERP format, the convolution layers perform zero-padding at the image boundaries, which introduces boundary artifacts in the feature maps [27]. As a result, even if the panorama image $\mathrm { Y _ { e q u i } }$ is free from seams in pixel space, its latent representation $\operatorname { y } _ { \mathrm { { e q u i } } }$ contains a discontinuity (Fig. 3a). We posit that this discontinuity is the root cause of seam artifacts in the generated panorama.

![](images/c80cfb338f8069f7d37fd170d0b31bf1c5b1e8af03eb19faf716271c3aa8df1c.jpg)

<details>
<summary>flowchart</summary>

```mermaid
graph LR
    A["enc"] --> B["shift"]
    B --> C["dec"]
    style A fill:#f9f,stroke:#333
    style B fill:#bbf,stroke:#333
    style C fill:#dfd,stroke:#333
```
</details>

(a) Naive VAE encoding of the panorama image leads to boundary discontinuities in the latent space.

![](images/0b0d45719fb4fa79dbb35b688e5a8c4c26566f00a5d4b82b967845b7804d5fd8.jpg)

<details>
<summary>flowchart</summary>

```mermaid
graph LR
    A["Pad"] --> B["Encrop"]
    B --> C["Crop"]
    C --> D["Shift"]
```
</details>

(b) Our Circular Latent Encoding leads to seam-free latent representations.   
Fig. 3: Illustration of Circular Latent Encoding. The top row (a) shows the seam artifact from naive VAE encoding. Shifting the encoded panorama latent by $1 8 0 ^ { \circ }$ shows a sharp discontinuity at the center, resulting in gray line-like artifacts when decoded back to image. The bottom row (b) illustrates our solution. Before encoding, we apply circular padding to the panorama image. After encoding, the latents in the padded regions are dropped. The shifted latent is now free from discontinuity, providing a seamless latent representation for diffusion training.

To eliminate the seam discontinuity in the encoded latent, we propose Circular Latent Encoding. Before encoding the latent of a panorama data, we crop $w ^ { \prime }$ (set as $W / 8$ in our experiments) columns from its left and right regions, and pad them to the opposite side of the panorama to extend the boundary at both sides:

$$
\mathrm{y} _ {\text {equi}} ^ {\text {pad}} = \mathcal {E} (\operatorname{Concat} ([ \mathrm{Y} _ {\text {equi}} [ - w ^ {\prime}: ], \mathrm{Y} _ {\text {equi}}, \mathrm{Y} _ {\text {equi}} [: w ^ {\prime} ] ])). \tag {3}
$$

After encoding, we drop the latent corresponding to the padded regions. This ensures the input sequence length to DiT is unchanged, thus introducing no overhead to training and inference. This simple technique produces a seam-free latent space (Fig. 3b), which serves as the correct target for model training.

# 4 Experiments

In this section, we conduct extensive experiments to answer the following questions: (i) How well does 360Anything perform on perspective-to-360◦ image and video generation? (Sec. 4.1 and Sec. 4.2) (ii) How accurate are the camera FoV and orientation inferred by our model? (Sec. 4.3) (iii) What is the impact of each design choice in our framework? (Sec. 4.4)

# 4.1 Panoramic Image Outpainting

Implementation details. We fine-tune FLUX.1-dev [34], a state-of-the-art (SoTA) open weights text-to-image DiT. We use the Adam optimizer [31] with a learning rate of $5 \times 1 0 ^ { - 5 }$ and train with a batch size of 512 for 50k steps. At inference time, we use FLUX’s default sampler [44] with 50 sampling steps and timestep shifting of 3.16. See Appendix A.2 for more implementation details.

Table 1: Quantitative results of perspective-to-360◦ image generation on Laval Indoor and SUN360 datasets. We borrow baseline results from CubeDiff [29] and report CubeDiff results under the single text description setting for a fair comparison. 360Anything achieves a clear improvement across all metrics, only marginally lagging behind CubeDiff in terms of CLIP-FID on Laval Indoor. 

<table><tr><td rowspan="2">Method</td><td colspan="5">Laval Indoor</td><td colspan="5">SUN360</td></tr><tr><td>FID ↓</td><td>KID (×102) ↓</td><td>CLIP-FID ↓</td><td>FAED ↓</td><td>CS ↑</td><td>FID ↓</td><td>KID (×102) ↓</td><td>CLIP-FID ↓</td><td>FAED ↓</td><td>CS ↑</td></tr><tr><td>OmniDreamer [1]</td><td>71.0</td><td>5.17</td><td>23.9</td><td>19.2</td><td>-</td><td>92.3</td><td>8.89</td><td>51.7</td><td>30.4</td><td>-</td></tr><tr><td>PanoDiffusion [89]</td><td>58.6</td><td>4.08</td><td>26.6</td><td>106.8</td><td>-</td><td>52.9</td><td>3.51</td><td>28.9</td><td>98.0</td><td>-</td></tr><tr><td>Diffusion360 [14]</td><td>33.1</td><td>2.07</td><td>16.9</td><td>23.7</td><td>26.38</td><td>45.4</td><td>3.73</td><td>18.5</td><td>12.6</td><td>22.89</td></tr><tr><td>CubeDiff [29]</td><td>9.5</td><td>0.32</td><td>3.2</td><td>18.4</td><td>27.02</td><td>25.5</td><td>1.33</td><td>8.1</td><td>7.6</td><td>25.00</td></tr><tr><td>360Anything (ours)</td><td>8.0</td><td>0.22</td><td>4.6</td><td>9.8</td><td>29.21</td><td>22.4</td><td>1.27</td><td>7.3</td><td>3.8</td><td>28.07</td></tr></table>

Training data and augmentations. For a fair comparison against CubeDiff [29], the prior state-of-the-art, we train on the same datasets with captions from Gemini 2.5 Flash [16]. To handle input images with diverse camera setup at test time, we uniformly sample FoV in [30◦, 120◦], pitch in $[ - 6 0 ^ { \circ } , 6 0 ^ { \circ } ]$ , roll in [−15◦, 15◦], and use them to crop the conditioning perspective images for training. Following prior works, we also perform horizontal roll augmentation on the panorama image. We train the model to generate ERP images at 1024×2048 resolution.

Evaluation data and metrics. We follow the evaluation protocol proposed in CubeDiff and report results on the Laval Indoor [15] and SUN360 [91] datasets. To measure visual quality, we report Fréchet Inception Distance (FID) [21], Kernel Inception Distance (KID) [2], FID on CLIP [58] features (CLIP-FID), and FID on features of an auto-encoder fine-tuned on panorama images (FAED) [99]. Following prior works, FID, KID, CLIP-FID are computed on perspective crops from the generated ERP image, while FAED is computed directly on the generated ERP image. We also report CLIP-score (CS) [20] for text alignment.

Quantitative results. Table 1 compares 360Anything with several perspectiveto-panorama image generation baselines. In terms of visual quality, 360Anything substantially outperforms all baselines on both datasets across FID, KID, and FAED metrics. Although it marginally lags behind CubeDiff in CLIP-FID on Laval Indoor (4.6 vs. 3.2), it outperforms it on SUN360 (7.3 vs. 8.1) which has more complex scene layouts and textures. Notably, we achieve a significant improvement in FAED, reducing the error by nearly 50% compared to the stateof-the-art. FAED is the only metric evaluated on the entire panorama, and it shows that 360Anything generates 360◦ images with clearly better quality and geometry. Finally, our method also achieves the best CLIP-score, demonstrating its superior capability in adhering to prompts.

Qualitative results. We only compare with CubeDiff since other baselines lag behind by a large margin in all metrics. As CubeDiff is not open-sourced, we compare with samples from their website. CubeDiff leverages a cubemap representation with six faces, each with 90◦ FoV. It treats the conditioning image as the front face and denoises the other five faces. As shown in Figure 4 left, it sometimes generates visible seams between faces. In addition, when the input perspective image has an FoV smaller than 90◦, CubeDiff has to stretch the object at the boundary, leading to distorted air-balloons (middle) and mushrooms (right). In contrast, 360Anything accurately infers the camera parameters to place the input image in the ERP space, and produces objects with the correct structure.

![](images/200da9e0d66ff5c56028f232aeaabf315e158b1fb92ec0b3b30ad0f714835838.jpg)  
Fig. 4: Qualitative results of perspective-to-360◦ image generation. We show multiple perspective views projected from the panorama, where the image with the green border is the conditioning image. Due to the use of a cubemap representation, CubeDiff sometimes generates seams between faces (left). In addition, CubeDiff always assumes the input image has a $9 0 ^ { \circ } \mathrm { F o V } ;$ ; yet when the actual FoV is smaller, it has to stretch the objects at the image boundary. This leads to distorted object structure, e.g., the balloons (middle) and the mushroom (right). In contrast, 360Anything estimates the correct camera FoV and orientation of the input as shown by the green box on the panorama image, and thus produces much less distorted objects. Please check out our project page to view the generated panorama images interactively.

# 4.2 Panoramic Video Outpainting

Implementation details. We fine-tune Wan2.1-14B [79], a SoTA open source text-to-video DiT. We use the Adam optimizer with a learning rate of $1 \times 1 0 ^ { - 5 }$ and train with a batch size of 64 for 20k steps. For model inference, we run 50 sampling steps with Wan’s default sampler and a timestep shifting of 3.0.

Training data and augmentations. We take the same training data from Argus [46], run our video canonicalization pipeline, and caption using Gemini 2.5 Flash. We follow Argus to simulate camera trajectories to crop conditioning perspective videos in training. However, we note that simulated camera movement lacks diversity. Thus, we also incorporate camera trajectories extracted from real-world videos [59], which improves generalization to in-the-wild videos (see Fig. 13 in the Appendix). We train on 512×1024 resolution videos with 81 frames.

Evaluation data and metrics. We adopt the 101 testing videos from Argus [46]. The conditioning perspective videos come from two types of camera trajectories,

Table 2: Quantitative results of perspective-to-360◦ video generation. We follow Argus [46] to evaluate on two sets of camera trajectories. Imag., Aes., and Motion stand for the Imaging Quality, Aesthetic Quality, and Motion Smoothness metrics from VBench [26]. Since the exact eval split used in Argus is unavailable, we reproduce the eval split based on communication with the author (\* denotes results on it). We report both the original and our reproduced results for Argus, which closely match to validate our reproduced eval set. Our method outperforms all baselines across all metrics.

<table><tr><td rowspan="2">Method</td><td colspan="6">Real camera trajectory</td><td colspan="6">Simulated camera trajectory</td></tr><tr><td>PSNR↑</td><td>LPIPS↓</td><td>FVD↓</td><td>Imag.↑</td><td>Aes.↑</td><td>Motion↑</td><td>PSNR↑</td><td>LPIPS↓</td><td>FVD↓</td><td>Imag.↑</td><td>Aes.↑</td><td>Motion↑</td></tr><tr><td>Imagine360*[74]</td><td>21.00</td><td>0.2636</td><td>1398.9</td><td>0.4556</td><td>0.4658</td><td>0.9666</td><td>20.45</td><td>0.2719</td><td>1532.1</td><td>0.4485</td><td>0.4536</td><td>0.9725</td></tr><tr><td>Argus [46]</td><td>21.83</td><td>0.2409</td><td>1228.6</td><td>0.4939</td><td>0.4828</td><td>0.9802</td><td>21.50</td><td>0.2602</td><td>1100.1</td><td>0.4812</td><td>0.4784</td><td>0.9805</td></tr><tr><td>Argus* [46]</td><td>22.35</td><td>0.2310</td><td>1020.7</td><td>0.4971</td><td>0.4863</td><td>0.9728</td><td>21.10</td><td>0.2653</td><td>1127.2</td><td>0.4762</td><td>0.4682</td><td>0.9852</td></tr><tr><td>ViewPoint*[13]</td><td>23.25</td><td>0.1364</td><td>844.3</td><td>0.5293</td><td>0.5150</td><td>0.9881</td><td>22.77</td><td>0.1326</td><td>957.8</td><td>0.5105</td><td>0.5045</td><td>0.9827</td></tr><tr><td>360Anything (ours)</td><td>25.75</td><td>0.0468</td><td>483.4</td><td>0.5515</td><td>0.5427</td><td>0.9885</td><td>23.64</td><td>0.0846</td><td>432.9</td><td>0.5489</td><td>0.5394</td><td>0.9891</td></tr></table>

“A person wearing a dark shirt and a hat holds the camera and is walking down a busy city street.”

![](images/c52870bcf508f646c81b895aac6acbc5862ddd1343373ce365e1f7657358e723.jpg)  
Fig. 5: Qualitative results of perspective-to-360◦ video generation. Regions corresponding to the input conditioning video are highlighted in red. Both Imagine360 and Argus exhibit low visual quality and distortions. ViewPoint always places the conditioning video at the center of the output, and thus generates a rotated image when the video contains large camera motion, leading to distortions (e.g., people and buildings). In contrast, 360Anything generates stably canonicalized panorama videos, and accurately follows the text prompt to outpaint a person holding the camera. Please see our project page for better visual comparisons in the video format.

namely, simulated and extracted from real-world videos. To measure input preservation, we report PSNR and LPIPS [101] between ground-truth and generated panorama videos within regions covered by the perspective video. We also report FVD [76], Imaging Quality, Aesthetic Quality, and Motion Smoothness from VBench [26] to evaluate overall quality. Note that VBench metrics are computed on perspective crops (left, right, front, back) of the generated panorama videos.

Quantitative results. Table 2 compares 360Anything with recent perspectiveto-360◦ video generation methods. We outperform all baselines across all metrics on both subsets, often by a large margin. Surprisingly, while prior works construct “pixel-aligned” input by unprojecting the conditioning perspective video, our method achieves better PSNR and LPIPS, meaning that it learns to better preserve the conditioning perspective video in the output. The significantly lower FVD indicates that our generated panorama videos exhibit more natural spherical distortion in the ERP format. In addition, 360Anything achieves higher VBench scores, demonstrating its superior visual and motion quality.

![](images/b7a46dd293b94028c1143339a0712a6d53c4ce69483e9a08cb86bccce553c7b6.jpg)  
Fig. 6: Qualitative results of 3D scene reconstruction. Given an input monocular video (left), 360Anything outpaints the whole 360◦ viewpoint (middle), from which we can optimize a 3DGS (right). This allows fly-through exploration of the entire 3D scene. Please check out our project page to view 360◦ rendering of the 3DGS.

Qualitative results. As shown in Figure 5, 360Anything exhibits significantly higher visual quality than Imagine360 [74] and Argus [46]. Since ViewPoint [13] assumes the input video is always at the center of the ERP, it has to rotate the generated panorama given a tilted perspective video, leading to distortions (e.g., people and buildings). In contrast, our method generates canonicalized panorama frames that are both temporally consistent and distortion-free.

3D scene reconstruction. To demonstrate the 3D consistency of our generated videos, we distill the panoramic videos produced by 360Anything into a 3D Gaussian Splat (3DGS) [30]. We only test videos of static scenes such as indoor rooms [96, 105] as vanilla 3DGS cannot handle dynamic subjects. This process involves two main steps: (i) First, we employ rig-based COLMAP [63, 64] on the generated panoramic video to obtain camera poses. We project each video frame to cubemap faces and perform constrained bundle adjustment using a cubemap rig. (ii) Then, we train a vanilla 3DGS on the posed images. The scene reconstruction results are qualitatively shown in Figure 6. Given a monocular video with partial scene coverage, 360Anything outpaints the full 360◦ view that provides enough geometric cues for 3D reconstruction. This allows free exploration over the reconstructed scene, demonstrating its strong geometry consistency.

# 4.3 Single Image Camera Calibration

Experimental setup. To measure the camera parameters estimated by our model, we evaluate 360Anything (the image version) on several benchmarks on FoV and camera pose estimation. Given a testing image $\mathrm { X _ { p e r s } }$ , we generate a panorama image $\mathrm { Y _ { e q u i } }$ , and infer the camera metadata via exhaustive search:

Table 3: Camera FoV estimation results (in degrees). We borrow baseline numbers from the MoGe paper [85]. Our zero-shot FoV estimation approach outperforms several supervised baselines, and lagging only slightly behind stateof-the-art methods DUSt3R and MoGe. 

<table><tr><td rowspan="2">Method</td><td colspan="2">NYUv2</td><td colspan="2">ETH3D</td><td colspan="2">iBims-1</td></tr><tr><td>Mean ↓</td><td>Med. ↓</td><td>Mean ↓</td><td>Med. ↓</td><td>Mean ↓</td><td>Med. ↓</td></tr><tr><td>Perspective [28]</td><td>5.38</td><td>4.39</td><td>13.6</td><td>11.9</td><td>10.6</td><td>9.30</td></tr><tr><td>WildCam [106]</td><td>3.82</td><td>3.20</td><td>7.70</td><td>5.81</td><td>9.48</td><td>9.08</td></tr><tr><td>LeReS [98]</td><td>19.4</td><td>19.6</td><td>8.26</td><td>7.19</td><td>18.4</td><td>17.5</td></tr><tr><td>UniDepth [56]</td><td>7.56</td><td>4.31</td><td>10.7</td><td>9.96</td><td>11.9</td><td>5.96</td></tr><tr><td>DUSt3R [86]</td><td>2.57</td><td>1.86</td><td>5.77</td><td>3.60</td><td>3.83</td><td>2.53</td></tr><tr><td>MoGe [85]</td><td>3.41</td><td>3.21</td><td>2.50</td><td>1.54</td><td>2.81</td><td>1.89</td></tr><tr><td>360Anything</td><td>3.90</td><td>3.17</td><td>5.68</td><td>4.22</td><td>5.21</td><td>4.04</td></tr></table>

Table 4: Camera pose estimation results (in degrees). Baseline numbers are from GeoCalib [77]. Our zero-shot approach outperforms most supervised baselines and lags behind current state-of-the-art, Geo-Calib, by just ∼0.5 degrees. 

<table><tr><td rowspan="2">Method</td><td colspan="2">MegaDepth</td><td colspan="2">LaMAR</td></tr><tr><td>Roll ↓</td><td>Pitch ↓</td><td>Roll ↓</td><td>Pitch ↓</td></tr><tr><td>MSCC [70]</td><td>0.90</td><td>5.73</td><td>1.44</td><td>3.02</td></tr><tr><td>ParamNet [28]</td><td>1.17</td><td>3.99</td><td>0.93</td><td>2.15</td></tr><tr><td>UVP [53]</td><td>0.51</td><td>4.59</td><td>0.38</td><td>1.34</td></tr><tr><td>GeoCalib [77]</td><td>0.36</td><td>1.94</td><td>0.28</td><td>0.87</td></tr><tr><td>360Anything</td><td>0.87</td><td>2.56</td><td>0.68</td><td>1.23</td></tr></table>

$$
\min _ {\text {fov,pitch,roll}} \| \mathrm{X} _ {\text {pers}} - \text {pano2pers(Y} _ {\text {equi}}; \text {fov,pitch,roll)} \| ^ {2},
$$

where pano2pers() is the panorama-to-perspective image projection function, and (fov, pitch, roll) are the estimated camera metadata.

Results on FoV estimation. We follow MoGe [85] and test our model on three real-world datasets: NYUv2 [68], ETH3D [65], and iBims-1 [33]. Table 3 compares 360Anything with several baselines. It is worth mentioning that all baseline models are trained on large-scale datasets specifically for 3D understanding tasks, while 360Anything is only trained for image-to-360◦ outpainting. In addition, over 90% of our training images are indoor scenes [103], thus creating a large domain gap with the outdoor ETH3D and iBims-1 datasets. Nevertheless, our zero-shot FoV estimation approach ranks among top-3 on most of the datasets. It achieves a low average estimation error of only 4.93 degrees, outperforming several supervised baselines while only lagging slightly (by 1 − 2 degrees) behind recent methods DUSt3R [86] (4.06) and MoGe (2.91).

Results on camera orientation estimation. We follow GeoCalib [77] to test our model on MegaDepth [38] and LaMAR [62] datasets for estimating camera roll and pitch angles from a single image. As shown in Table 4, our zero-shot approach clearly outperforms most supervised baselines, only lagging behind the current state-of-the-art GeoCalib (∼0.5 degrees in both roll and pitch). These results demonstrate that our method learns to establish accurate correspondence between the conditioning perspective input and the generated panorama image.

# 4.4 Ablation Study

Circular latent encoding. Table 5 and Figure 7 compare different seam elimination techniques. We report the discontinuity score (DS) [9] to quantify seam artifacts. Our circular latent encoding (CLE) dramatically reduces DS compared to the blended decoding approach proposed in Argus [46]. In addition, our method introduces no overhead to the generation process.

Table 5: Comparison of seam elimination techniques in perspective-to-360◦ image and video tasks. We report the discontinuity score (DS) to measure seams at the boundary of the panorama. CLE stands for our Circular Latent Encoding technique. 

<table><tr><td rowspan="2">Method</td><td colspan="3">Image</td><td colspan="3">Video</td></tr><tr><td>Vanilla</td><td>Blended Decoding</td><td>CLE (ours)</td><td>Vanilla</td><td>Blended Decoding</td><td>CLE (ours)</td></tr><tr><td>DS↓</td><td>9.92</td><td>5.29</td><td>3.87</td><td>35.52</td><td>19.84</td><td>13.28</td></tr></table>

Vanilla   
![](images/f27c0029442b369f705fc497319f8faac3e76a64cb6b26a15add5f656be6c491.jpg)

<details>
<summary>natural_image</summary>

Interior view of a dimly lit room with warm lighting, featuring a bed, furniture, and a red-framed camera (no visible text or symbols)
</details>

Blended Decoding   
![](images/a4faf2c8d8dbb456d63657324e2b41049f1195dda03bdd1274a4411b540241cd.jpg)

<details>
<summary>natural_image</summary>

Interior view of a dimly lit bedroom with furniture and lighting (no visible text or symbols)
</details>

Circular Latent Encoding (ours)   
![](images/082eecf124903eb34df072f2e8b2a64e9cd4862718a21718fb907f64f41e3cd5.jpg)

<details>
<summary>natural_image</summary>

Interior panoramic view of a bedroom with bed, lamp, and wall-mounted furniture (no visible text or symbols)
</details>

![](images/0cbf25535071771475fb31558fc5decb9d4eba05862430c66435b8b7f9b28880.jpg)

<details>
<summary>natural_image</summary>

Interior view of a modern living room with red curtains and furniture (no visible text or symbols)
</details>

![](images/55df5efb7c1a0a2768b969ff2368c9c903dd35e42224f17387bff884209b6c06.jpg)

<details>
<summary>natural_image</summary>

Interior view of a living room with sofa, coffee table, TV, and wall art (no visible text or symbols)
</details>

![](images/d6db2dc7bbdb6a0278f658d708cd43ffd78daea0ebb5b550cd3f43cb1896379f.jpg)

<details>
<summary>natural_image</summary>

Interior view of a modern living room with sofas, curtains, and furniture (no visible text or symbols)
</details>

Fig. 7: Qualitative evaluation of various seam elimination techniques. For ease of visualization, we shift the generated panorama by $1 8 0 ^ { \circ }$ to show the concatenation of its left and right boundaries. Without any intervention (left), there are clear seams. Blended Decoding [46] (middle) “blurs” the seam to remove discontinuities; yet, it introduces gray line-like artifacts. Our technique (right) eliminates boundary artifacts entirely. We recommend zooming-in to evaluate these differences appropriately.

Camera augmentation. Training with randomly sampled camera FoVs and orientations enables 360Anything to handle arbitrary perspective input at test time. However, the common evaluation protocol of perspective-to-360◦ image generation always uses conditioning images with 90◦ FoV and zero pitch and roll. We thus study whether disabling random camera sampling can further improve model performance. Surprisingly, Table 6 shows that such camera augmentation improves results on all metrics. We hypothesize that training with a wide distribution of camera setup forces the model to better understand perspective-equirectangular geometry, preventing overfitting to a single mapping.

Robustness to camera parameters. We condition 360Anything on perspective images with different camera FoVs and orientations. As shown in Table 6, all metrics improve as FoV increases, as a larger FoV gives more information about the entire panorama image. Performance degrades slightly when changing pitch and roll angles, yet the degradations are all less than 1.0. We further compare with a variant of 360Anything that uses channel-concatenation instead of sequenceconcatenation for conditioning (dubbed CC w/ GT Camera). At test time, it uses ground-truth camera metadata to perform the perspective-to-equirectangular projection. Table 6 shows that it suffers from a similar performance drop as 360Anything; yet our method does not rely on camera metadata. Overall, these results demonstrate the robustness of 360Anything.

Table 6: Robustness to input view variations. We report the absolute FID/FAED for the standard view $( 9 0 ^ { \circ } , 0 ^ { \circ } , 0 ^ { \circ } )$ and the relative change in metrics (∆) for other input views. The last column shows the average degradation across all input view variations. w/o Camera Aug. stands for our method trained without camera augmentation, i.e., always train with $( 9 0 ^ { \circ } , 0 ^ { \circ } , 0 ^ { \circ } )$ as the conditioning view. CC w/ GT Camera denotes the channel concatenation model that has access to ground-truth camera information. 360Anything shows similar robustness to it even without camera metadata.

<table><tr><td rowspan="2">Metric</td><td rowspan="2">Method</td><td colspan="7">Conditioning View (FoV, Pitch, Roll)</td><td rowspan="2">Avg.</td></tr><tr><td>(90, 0, 0)</td><td>(30, 0, 0)</td><td>(60, 0, 0)</td><td>(90, 30, 0)</td><td>(90, -30, 0)</td><td>(90, 30, 5)</td><td>(90, -30, -5)</td></tr><tr><td rowspan="3">FID ↓</td><td>w/o Camera Aug.</td><td>8.4</td><td>+2.4</td><td>+0.8</td><td>+8.9</td><td>+9.2</td><td>+8.9</td><td>+8.7</td><td>+6.48</td></tr><tr><td>CC w/ GT Camera</td><td>7.7</td><td>+3.4</td><td>+0.8</td><td>+0.5</td><td>+0.9</td><td>+0.5</td><td>+0.9</td><td>+1.17</td></tr><tr><td>360Anything (ours)</td><td>8.0</td><td>+2.2</td><td>+0.8</td><td>+0.6</td><td>+0.9</td><td>+0.6</td><td>+0.8</td><td>+0.98</td></tr><tr><td rowspan="3">FAED ↓</td><td>w/o Camera Aug.</td><td>10.4</td><td>+6.6</td><td>+1.7</td><td>+1.4</td><td>+1.8</td><td>+1.4</td><td>+1.6</td><td>+2.42</td></tr><tr><td>CC w/ GT Camera</td><td>9.9</td><td>+5.1</td><td>+1.5</td><td>+0.4</td><td>+0.4</td><td>+0.3</td><td>+0.2</td><td>+1.32</td></tr><tr><td>360Anything (ours)</td><td>9.8</td><td>+5.4</td><td>+1.5</td><td>+0.3</td><td>+0.4</td><td>+0.3</td><td>+0.4</td><td>+1.38</td></tr></table>

Table 7: Ablation on training video canonicalization. To save compute, models are trained at a lower resolution of 256×512. Imag., Aes., and Motion stand for Imaging Quality, Aesthetic Quality, and Motion Smoothness from VBench [26]. Training on canonical videos improves visual quality as indicated by FVD and VBench metrics.

<table><tr><td rowspan="2">Canonical</td><td colspan="6">Real camera trajectory</td><td colspan="6">Simulated camera trajectory</td></tr><tr><td>PSNR↑</td><td>LPIPS↓</td><td>FVD↓</td><td>Imag.↑</td><td>Aes.↑</td><td>Motion↑</td><td>PSNR↑</td><td>LPIPS↓</td><td>FVD↓</td><td>Imag.↑</td><td>Aes.↑</td><td>Motion↑</td></tr><tr><td>No</td><td>26.56</td><td>0.0491</td><td>559.5</td><td>0.4689</td><td>0.4939</td><td>0.9894</td><td>24.66</td><td>0.0656</td><td>527.0</td><td>0.4601</td><td>0.4917</td><td>0.9888</td></tr><tr><td>Yes (ours)</td><td>24.02</td><td>0.0521</td><td>470.8</td><td>0.5437</td><td>0.5180</td><td>0.9899</td><td>22.39</td><td>0.0880</td><td>449.8</td><td>0.5387</td><td>0.5154</td><td>0.9903</td></tr></table>

Training video canonicalization. We ablate the effect of training on canonicalized panorama videos in Table 7. When training on non-canonicalized videos, we always place the conditioning view at the center of the output image. This makes reconstructing the conditioning frame much easier, leading to better PSNR and LPIPS. However, it degrades the visual quality and fidelity significantly, as clearly indicated by FVD and VBench metrics. This is because the model has to generate panorama frames with varying gravity directions when the input video has non-zero roll and pitch angles, forcing it to learn different spherical distortion patterns. In contrast, we train 360Anything to generate gravity-aligned upright panoramas, which greatly simplifies the generation task.

# 5 Conclusion

We present 360Anything, a geometry-free framework for in-the-wild perspectiveto-panorama generation. By shifting from explicit geometric unprojection to simple sequence concatenation within a DiT, we eliminate the dependency on camera information, allowing the model to learn geometric correspondence purely from data. Furthermore, we identify VAE encoder padding as the root cause of seam artifacts and introduce a novel and principled fix. Our approach not only achieves state-of-the-art results on panoramic image and video benchmarks, but also demonstrates robust zero-shot generalization to diverse, real-world media. We discuss the limitations and future directions in Appendix C.

# Acknowledgment

We would like to thank Nikolai Kalischek for help with the image data and Rundong Luo for help with the video data. We thank Noah Snavely and Richard Tucker for implementing the pipeline for running structure-from-motion at scale. We thank George Kopanas for help with the 3DGS experiments, Sara Sabour for discussion on 3D SfM, and Lala Li for guidance on Gemini captioning. We thank Charles Herrmann and Jon Shlens for providing feedback during the internal review process. We also thank the maxdiffusion contributors for converting FLUX and Wan from PyTorch to Jax. Some testing videos are from Veo, Sora, Gen4.5, and Wan. We thank them for sharing the videos.

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# A Detailed Experimental Setup

In this section, we provide full details on the datasets, baselines, evaluation settings, and the training and inference implementation details of our model.

# A.1 Training Data

Image data. To facilitate a fair comparison with the previous state-of-the-art method, CubeDiff [29], we follow them to use the same panorama image datasets as training data. These include Polyhaven [57], Humus [55], Structured3D [103], and Pano360 [32]. For Structured3D, we use all three subsets, namely, empty, simple, and full. As a result, around 90% of data are synthetic rendering of indoor rooms from Structured3D. We then use Gemini 2.5 Flash [16] for captioning.

Data augmentation: to handle input images with diverse camera setup at test time, we uniformly sample FoV in [30◦, 120◦], pitch in $[ - 6 0 ^ { \circ } , 6 0 ^ { \circ } ]$ , roll in [−15◦, 15◦], and use them to crop the conditioning perspective images for training. We also perform horizontal roll augmentation on the panorama image.

Video data. We use the panorama videos from the 360-1M dataset [78] featuring YouTube videos. Specifically, we take the filtered subset from Argus [46], which removes videos with non-panorama format, very low motion, and bad visual quality. We then run our two-step video canonicalization pipeline (see Figure 8). First, we run COLMAP [64] with rig support to estimate per-frame camera pose, and then rotate each frame to eliminate inter-frame camera rotation (i.e., stabilize the video). Then, we run GeoCalib [77] to convert the video to a gravityaligned upright pose. Since the video is stabilized, all frames should share the same gravity direction. We thus predict the direction for all frames and then average the predictions after removing outliers (i.e., values more than 3 standard deviations from the mean). GeoCalib is only trained on perspective images; thus we project each panorama video frame to eight perspective images (elevation=0 with uniform azimuth), run GeoCalib on each image, and take an average after the same outlier removal technique. Finally, we rotate the video so that its gravity direction is aligned with the vertical axis. Similar to the image data, we use Gemini 2.5 Flash to caption the video (downsampled to 1 FPS). We apply the same pipeline to both the coarse and high-quality subset from Argus.

Data augmentation: to handle in-the-wild perspective video, we follow Argus to simulate camera trajectories with randomly sampled linear motion plus noise. However, we note that simple linear motion is not diverse enough, and models trained on it fail to generalize to videos with complex motion (see Fig. 13). Thus, we also incorporate camera trajectories extracted from real-world videos [59, 96] during training. We randomly sample from simulated (80%) and real-world (20%) trajectories to crop perspective videos as model conditioning.

# A.2 Implementation details

Image model. We fine-tune FLUX.1-dev [34], a popular open weights textto-image diffusion transformer model. The target panorama and conditioning perspective images are separately encoded with the VAE, flattened to 1D sequence of tokens, and concatenated along the sequence dimension as model input. We use the same spatial index (x, y coordinate of the token) to apply 3D RoPE [72] to conditioning and target tokens. To distinguish between them in the concatenated sequence, we offset the time dimension index by 1 when applying 3D RoPE to perspective tokens following [35, 87]. We fine-tune the entire model using the Adam optimizer [31] with a batch size of 512 for 50k steps. The learning rate linearly increases from 0 to $5 \times 1 0 ^ { - 5 }$ in the first 1k steps, and then stays constant. A gradient clipping of 1.0 is applied to stabilize training. To apply classifier-free guidance (CFG) [23], we randomly drop the text embedding of the caption and the conditioning image with a 10% probability during training. The model is trained on panorama images in the Equirectangular Projection (ERP) format with a resolution of 1024×2048.

![](images/91648cead58fe97fcce3a4487f131c580537a2991288e79c7ac07dc17f3af553.jpg)

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Raw
Stabilized
Canonical
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Fig. 8: Visualization of the video canonicalization pipeline. Top: Raw panorama frames exhibit varying elevation angles, causing the horizon to fluctuate relative to the reference line (red dashed). Middle: After stabilization, inter-frame rotation is removed, resulting in a temporally consistent horizon height across all frames. Bottom: After aligning the gravity direction to the vertical axis, the horizon is rectified to a straight line parallel to the image boundaries, ensuring an upright orientation. Please refer to our project page for better comparisons in video format.

Inference. We use FLUX’s default rectified flow sampler [44] with 50 sampling steps. FLUX computes timestep shifting based on the number of tokens. Images at 1024×2048 resolution surpasses its maximum number of tokens (4096), we thus uses its cutoff shifting value of $\exp ( 1 . 1 5 ) \approx 3 . 1 6$ . We tried larger value but observed degradation in the result. We apply CFG on both text and image similar to [4], with a scale of 2.0 on text and 1.5 on image.

Video model. We fine-tune Wan2.1-14B [79], a popular open source text-tovideo diffusion transformer model. Most of the model design is the same as the image model. The only difference is that, in 3D RoPE, we offset the time dimension index of perspective tokens by 0.1 rather than 1 to avoid confusion with tokens from subsequent frames. We fine-tune the entire model using the Adam optimizer with a learning rate of $1 \times 1 0 ^ { - 5 }$ , and a batch size of 64. The same warmup schedule, gradient clipping, and CFG dropping are applied. The model is first trained on ERP videos with 81 frames, 256×512 resolution from the coarse subset for 10k steps, and then on ERP videos with 81 frames, 512×1024 resolution from the high-quality subset for another 10k steps.

Inference. We run 50 sampling steps with Wan’s default sampler and timestep shifting of 3.0, which outperforms 2.0 and 5.0 in our ablation. We use a CFG weights of 3.0 for text and 2.0 for conditioning on perspective video.

# A.3 Evaluation Setup

Perspective-to-360◦ image generation. We evaluate on the Laval Indoor [15] and SUN360 [91] datasets. To measure visual quality, we report Fréchet Inception Distance (FID) [21], Kernel Inception Distance (KID) [2], FID on CLIP [58] features (CLIP-FID), and FID on features of an auto-encoder fine-tuned on panorama images (FAED) [99]. In line with CubeDiff, FID, KID, and CLIP-FID are computed on 10 perspective crops (10 azimuth angles randomly sampled from $[ 9 0 ^ { \circ } , 2 7 0 ^ { \circ } ]$ to avoid overlap with the input view whose elevation=0◦) from the generated panorama using the clean-fid package [52]. Meanwhile, FAED is computed directly on the entire panorama as it measures the overall geometry of the ERP. We adopt the implementation from PanFusion [99]1. We also report CLIP-score (CS) [20] between captions and the ERP images for text alignment.

Perspective-to-360◦ video generation. We follow Argus and use a hold out set of 101 videos as evaluation data. However, as the exact eval split from Argus is unavailable, we reproduce the eval set based on offline communication with the authors. We tested Argus on this reproduced eval set and ensured that the metrics are comparable to those reported in the paper. Following Argus, we use two types of camera trajectories, namely, simulated and extracted from real-world videos, to obtain conditioning videos. To measure fidelity, we report PSNR and LPIPS [101] between ground-truth and generated panorama videos within regions covered by the perspective video. Concretely, it is computed by projecting a mask to the ERP space using ground-truth camera information at each step, and then take a union over all steps. We also report FVD [76] on the ERP videos to measure the overall geometry and visual quality. Finally, we adopt Imaging Quality, Aesthetic Quality, and Motion Smoothness from VBench [26] to evaluate overall generation quality. VBench metrics are computed on perspective projections (front, left, right, back) of the generated panorama videos.

# A.4 Baselines

Perspective-to-360◦ image generation. Since we follow the evaluation setup of the previous state-of-the-art (CubeDiff), we borrow the number of baselines from their paper. Here, we only discuss CubeDiff. CubeDiff leverages a cubemap representation, which projects the panorama image to six perspective views (six faces of a cube), each with 90◦ FoV. At inference time, it inputs the conditioning image as the front face, and denoises the other five faces jointly. This means they always assumes the input image has a 90◦ FoV and lies at the center of the generated panorama, preventing them from adapting to in-the-wild images with arbitrary FoV and camera orientation.

![](images/3acde9c721ca0863bccd4b15c11a22f4318eeb972298219f2a4350ef31cc6c6e.jpg)

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Lighthouse on a snowy, icy landscape under a vibrant green and purple aurora (no text or symbols visible)
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![](images/627244c0b6bc2ef0e6d29ab4d76319948ce78b3b9e463a9303a9464b9ea9080a.jpg)

![](images/5ad4672382c61fc7f04ae82b9f151a5dd16dc2705f3047abf22d9be3e8725552.jpg)

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Interior view of an empty industrial hall with arched ceilings and large windows (no visible text or symbols)
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![](images/f5ab1e155f491e96fcb83355916fea6b12bc68f645fb6a3df0cc107e1c16fb53.jpg)

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Panoramic view of a forested hillside with large rock formations and green foliage, no visible text or symbols
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![](images/b2a54b5da48f1d310e4e8f9f29e98b4a6f55503cd15399ac258cdef12e8064b5.jpg)

![](images/bed7d774c46c907678979b81dee592a549fb58ffb331330340c0ebbc0b08b6d8.jpg)

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Illustration of a vibrant, fantasy mushroom garden with visitors and floating plants under a bright sky (no text or symbols)
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Fig. 9: More perspective-to-360◦ image generation results from 360Anything. We test on out-of-distribution images, such as AI-generated ones. The conditioning perspective images are shown at the middle.

Perspective-to-360◦ video generation. We compare with three recent works with released code. Notably, if the method requires camera metadata to project the perspective video to the ERP space, we always use ground-truth camera metadata. This provides additional information compared to 360Anything.

– Imagine360 [74] leverages a dual-branch architecture based on AnimateDiff [18] to process perspective and ERP data, connected with spherical attention. We leverage their official code.   
– Argus [46] projects the perspective video to the ERP space, and fine-tunes SVD [3] to outpaint the entire panorama video. We leverage their official code.   
– ViewPoint [13] is inspired by the cubemap representation, and designs a viewpoint map representation with less spherical distortion compared to ERP. It then fine-tunes Wan2.1 [79] on this new representation. However, similar to CubeDiff, ViewPoint also places the conditioning video at the front view, leading to severely rotated panoramas when the input video has large camera motion. We leverage their official code.

# B More Experimental Results

# B.1 Failure Cases of Channel-Concatenation Baselines

A popular line of work in perspective-to-360◦ generation first projects the perspective input to the ERP space, and then concatenates it with the noisy target latent channel-wise as model input. This requires external models to estimate the FoV and camera orientations for the projection, which is tedious from a user perspective. Moreover, channel-concatenation models may suffer from mistakes made by off-the-shelf camera estimators. As shown in Figure 14, when the input video has complex camera trajectories or lighting conditions, even SoTA camera estimators like MegaSaM [39] fail. In the first two examples, the predicted roll angles drift significantly, leading to severely tilted objects in the conditioning view. In the last example, the predicted FoV is too small, making the projected view uninformative. Argus is unable to correct the out-of-distribution conditioning input, and generates broken results. In contrast, 360Anything gets rid of explicit camera information with a sequence-concatenation mechanism, and can still generate reasonable panorama videos from these challenging videos.

![](images/38179f652ae88549caaf2972839206cc50696f4c84d78675ca88952f87d5f158.jpg)  
Fig. 10: Qualitative results of 3D scene reconstruction. Given an input monocular video (left), 360Anything outpaints the whole 360◦ viewpoint (middle), from which we can optimize a 3DGS (right). This allows fly-through exploration of the entire 3D scene.

# B.2 Perspective-to-360◦ Image Generation

We present results on out-of-distribution perspective images in Figure 9. The conditioning images are generated by text-to-image models. Despite mainly trained on indoor synthetic data, 360Anything still generalizes to these OOD samples with high visual quality and correct overall structure.

# B.3 Perspective-to-360◦ Video Generation

Panoramic video outpainting. We show more qualitative comparisons in Figure 15. In the first two examples, the conditioning perspective videos have large elevation changes (moves up and down). Imagine360 and Argus handle this by projecting the perspective view to the ERP space as model conditioning. Yet, they still generate videos with low visual quality due to the use of poor video backbones [3, 18]. ViewPoint uses the same Wan [79] backbone as us. However, it always treats the input frames as the front-view of the cubemap representation, and thus has to generate significantly rotated panoramas. This leads to severely distorted humans and objects. Thanks to the canonicalization training objective, 360Anything produces videos that are always upright, maintaining the correct scene geometry. In the last example, the conditioning video contains a partially observed hand. All baselines fail to outpaint the entire person, and only generate a small part of an arm. In contrast, 360Anything outpaints the entire person following the text prompt, and still maintains it when the hand moves out of the input view. This shows the strong world knowledge of our method.

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Fig. 11: Panorama video generation given large motion videos. Regions corresponding to the input conditioning video are highlighted in red. We test on perspective videos with large object or camera motion.   
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Four-panel panoramic view of a surreal underwater scene with a figure in a seated pose, surrounded by water and coral (no visible text or symbols)
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Fig. 12: Panorama video generation on AI generated videos. Regions corresponding to the input conditioning video are highlighted in red. We test on perspective videos generated by other video models.

3D scene reconstruction. We show more qualitative results in Figure 10. Given a narrow field-of-view video from RealEstate10K [105], 360Anything synthesizes the entire 360◦ view of the room. We can then train a 3D Gaussian Splatting model [30] on the generated panoramas for novel view synthesis. This demonstrates the high 3D consistency of our generated videos.

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Panoramic interior photos comparing modern and unmodern office spaces with 'w/o real-world' and 'Ours' labels, showing room layouts and lighting (no text or symbols in the image itself)
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Fig. 13: Ablation on real-world camera trajectories. 360Anything uses both simulated and real-world camera trajectories to crop perspective videos in model training. Models without this setup generate panorama frames with changing gravity directions, fail to produce canonicalized videos and may suffer from broken structure.

Results on large motion videos. We stress test our model on perspective videos with large object or camera motion. Figure 11 shows that 360Anything is still able to produce temporally consistent videos that are in a stable canonical pose. This shows that our model understands complex geometry and is able to find correspondence across the 4D world.

Results on OOD videos. We test our model on perspective videos generated by other video models, including Wan, Sora, Veo, and Runway Gen-4.5. Figure 12 shows that 360Anything still generates panorama videos with high quality. Notice how the reflection of billboards on water is well handled in the first example, even though it is not present in the input. In the second example, the dust caused by the car persists after the car drives by. In the third example, the model generates the statue underwater when the camera is above the water surface. The last example features stylized black-and-white footage.

Ablation on real-world camera trajectories. We tested the model on inthe-wild perspective videos with large camera motion. Large camera motion makes maintaining a canonicalized panorama output more challenging. As shown in Figure 13, models trained only with simulated camera trajectories fail to produce frames with changing gravity directions. In contrast, 360Anything with real-world camera trajectories generates stably canonicalized panorama videos.

# C Limitations and Future Work

360Anything is fine-tuned from a pre-trained video diffusion model, and thus we are bounded by the capacity of the base model. For example, it is challenging to outpaint scenes involving complex physics. In addition, we also inherit the bias in its training data. For example, the model sometimes generates panorama videos with black borders or undesired objects (e.g., a tripod or a human’s hand) at the bottom of the video since they are common in YouTube 360◦ videos.

Due to the high resolution of panorama data (an ERP video has 8× number of pixels compared to a normal perspective video) and the limited compute, our current video model can only handle videos with 81 frames. A larger context window will enable larger-scale 3D world generation. An interesting future direction is combining 360Anything with recent progress in long video generation that distills bi-directional DiTs to causal autoregressive DiTs [7, 24, 97]. To obtain higher-resolution panoramas, we tried existing video upsamplers designed for perspective videos [67]. However, this often re-introduces seams at the ERP boundary, and distorts the structure of the ERP space, calling for research in panorama upsampling techniques.

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Fig. 14: Perspective-to-360◦ video generation on challenging input videos. MegaSaM [39] fails to predict the correct camera poses (first two examples) or FoV (last example), leading to degraded generation results from Argus. In contrast, our method runs end-to-end without a projection stage and thus generalizes well.

“A man walks through a narrow street in a residential area. A truck, several cars and motorbikes are present.”

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“Near a harbor surrounded by a village and rolling hills, an old man is actively rowing with two white oars.”

agine36   
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Argus   
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Person rowing a boat on water with hills in the background, no visible text or symbols
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Person riding a boat with a person sitting on top, overlooking a coastal cityscape (no visible text or symbols)
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urs   
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Person sitting on a boat deck with floating umbrellas, overlooking a lakeside with hills and buildings in the background (no visible text or symbols)
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Person standing on a boat deck surrounded by water and hills, viewed through a fisheye lens (no text or symbols visible)
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Panoramic view of a person standing on a boat deck overlooking a lake with boats and distant hills (no visible text or symbols)
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Panoramic view of a person standing on a floating platform overlooking a river with boats and hills in the background (no visible text or symbols)
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“A man in a dark grey t-shirt gestures towards a large green tank inside a dimly lit room.”

agine36   
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Interior view of a brightly lit transportation hub with illuminated machinery and crowds (no visible text or symbols)
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Interior view of a brightly lit hotel lobby with illuminated seating and decorative decorations (no visible text or signage)
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Interior view of a futuristic entertainment space with glowing red lighting and a central green character (no visible text or symbols)
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Interior view of a dimly lit indoor space with illuminated furniture and people (no visible text or symbols)
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Argus   
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Three green glass laboratory funnels with glowing blue liquid, no visible text or symbols
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Interior view of a modern, ornate hall with arched ceiling and interior lighting (no visible text or symbols)
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Interior view of a futuristic exhibition space with green-themed installations and visitors (no visible text or symbols)
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urs   
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Interior of a grand hall with a person interacting with a green glass lamp (no visible text or symbols)
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Interior view of a modern exhibition hall with a person interacting with a green-lit display (no visible text or symbols)
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Interior view of a futuristic museum or exhibition space with visitors observing a glowing green glass artifact (no visible text or symbols)
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Interior view of a futuristic exhibition hall with visitors observing a central green-lit display (no visible text or symbols)
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Fig. 15: Qualitative comparisons of perspective-to-360◦ video generation.