Title: ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Diffusion Models
Abstract: Panoramic video generation aims to synthesize 360-degree immersive videos, holding significant importance in the fields of VR, world models, and spatial intelligence. Existing works fail to synthesize high-quality panoramic videos due to the inherent modality gap between panoramic data and perspective data, which constitutes the majority of the training data for modern diffusion models. In this paper, we propose a novel framework utilizing pretrained perspective video models for generating panoramic videos. Specifically, we design a novel panorama representation named ViewPoint map, which possesses global spatial continuity and fine-grained visual details simultaneously. With our proposed Pano-Perspective attention mechanism, the model benefits from pretrained perspective priors and captures the panoramic spatial correlations of the ViewPoint map effectively. Extensive experiments demonstrate that our method can synthesize highly dynamic and spatially consistent panoramic videos, achieving state-of-the-art performance and surpassing previous methods.

ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models

Abstract

Panoramic video generation aims to synthesize 360-degree immersive videos, holding significant importance in the fields of VR, world models, and spatial intelligence. Existing works fail to synthesize high-quality panoramic videos due to the inherent modality gap between panoramic data and perspective data, which constitutes the majority of the training data for modern diffusion models. In this paper, we propose a novel framework utilizing pretrained perspective video models for generating panoramic videos. Specifically, we design a novel panorama representation named ViewPoint map, which possesses global spatial continuity and fine-grained visual details simultaneously. With our proposed Pano-Perspective attention mechanism, the model benefits from pretrained perspective priors and captures the panoramic spatial correlations of the ViewPoint map effectively. Extensive experiments demonstrate that our method can synthesize highly dynamic

and spatially consistent panoramic videos, achieving state-of-the-art performance and surpassing previous methods.

1 Introduction

Imagine you're traveling-would you seize the chance to capture the breathtaking landscapes that unfold before you? When you return home, do you long to relive and immerse yourself in those vivid experiences once more? Recently, omnidirectional vision has garnered increasing attention as it unlocks immersive AR/VR, virtual travel, and telepresence experiences. However, recording 360-degree videos requires expensive professional devices, i.e., 360 cameras, making the creation of panoramic content challenging, and most consumers capture only narrow field-of-view (FoV) clips on portable monocular cameras, e.g., smartphones. Enabling these perspective recordings to become full panoramas would democratize spherical media, letting anyone relive or share memories in true 360-degree form.

Achieving this conversion is non-trivial due to fundamental representation gaps between perspective and panorama domains. A straightforward approach is to adopt the widely-used Equirectangular Projection (ERP) for panoramas, which maps the spherical view onto a rectangular image. Unfortunately, this format introduces severe distortions, especially near the poles, stretching and squashing content unnaturally. More critically, equirectangular images lie outside the distribution of typical training data for modern generative models, which are at the center of the recent surge of interest in computer vision and pattern recognition [45, 27, 28, 42, 41, 19-24]. Diffusion models [10, 29, 32] and VAEs [14] are predominantly trained on perspective imagery, so without significant adaptation they struggle to produce high-quality results in the warped ERP space. On the other hand, one could represent the panorama as multiple perspective projections. A common choice is the Cubemap Projection(CP) format, which unfolds the sphere into six faces (each a 90° FOV perspective view). The CP representation avoids polar distortions and yields locally planar patches well-aligned with the priors of convolutional and diffusion networks. However, a naive cubemap suffers from spatial discontinuities at the borders of the faces. The six faces are disjoint on a 2D grid, making it difficult for a neural model to capture cross-face consistency. In short, existing representations force a trade-off between continuity and distortion: ERP offers end-to-end continuity but distorts the content, whereas CP preserves local fidelity but fragments the panoramic space. Neither is ideal for generative video modeling, especially when temporal consistency is also required.

In this paper, we address the above-mentioned issues by introducing ViewPoint, a novel 360° video representation and generation framework that bridges the strengths of ERP and CP. At the core of ViewPoint is a spatially-aware pseudo-stitching scheme that reprojects and rearranges the scene into an overlapping set of perspective views with greatly improved continuity. Specifically, we first convert the panorama into six cube faces-each a distortion-free perspective view-and then merge them into a small number of overlapping "pseudo-perspective" panels. Because these panels share content along their boundaries, they eliminate the hard seams of a standard cubemap while remaining fully compatible with existing 2D diffusion models. On this representation, the proposed Pano-Perspective attention delivers two key benefits: (1) Global coherence: Pano-attention blocks span the entire stitched map (and time), ensuring that opposite directions align and the scene stays consistent. (2) Local fidelity: Perspective-attention blocks focus on each panel's neighborhood, preserving fine texture, color, and motion details. With this attention mechanism, we take full advantage of the generative capabilities of diffusion models and adapt them to the task of panoramic video generation, where high-quality data is scarce.

In summary, our contributions are as follows:

We introduce a novel representation for 360-degree content aimed at improving spatial continuity and reducing distortion while leveraging the power of diffusion models to generate 360-degree videos through the proposed format.

We design a Pano-Perspective attention mechanism, enabling the model to simultaneously maintain global spatial continuity across the entire panorama and significantly enhance the preservation of fine-grained details and motion.

Extensive experiments demonstrate that our method can generate high-quality 360-degree videos and achieve state-of-the-art performance, outperforming previous approaches.

2 Related Works

Panorama Representations. Panoramic images are often projected from spherical space to the 2D plane for processing and storage [1]. Equirectangular Projection (ERP) is the most popular format, which uniformly maps pixels from a spherical surface to a planar rectangle. Despite its simplicity and spatial continuity, ERP inevitably brings disadvantages such as geometric distortion at the poles. Cubemap Projection (CP) , on the other hand, projects panoramas to six cube faces with the FoV of 90 ◦ × 90 ◦ to alleviate geometric distortion. However, under this representation, only one cube face is explicitly spatially continuous with its four adjacent faces on the 2D plane.

Panorama Generation. Panoramic image generation [13, 34, 40, 44, 8, 6, 18, 46, 38, 30, 37, 15] aims to synthesize 360-degree immersive images based on user-provided textual or visual clues. It requires the generated images to be seamless in spherical space while maintaining rationality in any perspective anchor. Recent studies [13, 34, 40, 44, 46, 38, 37, 15] leverage powerful image diffusion models to generate high-quality panoramic images. Benefiting from the great generative capabilities of diffusion models, these works have made significant advancements in omnidirectional image synthesis. Among them, PanoGen [15] achieves high-quality results in indoor scenarios by introducing a recursive outpainting and stitching mechanism. PanFusion [46] proposes a dualbranch pipeline to integrate equirectangular features and perspective features. CubeDiff [13] uses an alternative representation, cubemap, to synthesize panoramic images with high fidelity and diversity. Despite the significant progress in omnidirectional image generation, achieving similar success in the field of video remains a challenging problem, as it requires spatial-temporal consistency across the entire spherical space, as well as costly computational resources.

A few works [39, 16, 25, 33] explore panoramic video generation. 4K4DGEN [16] animates a given high-resolution panoramic image through user intention. However, the requirement of highquality panoramic inputs limits the application scenarios, as it struggles to generate omnidirectional content without the given images. VidPanos [25] extends a given video to a larger FoV by using Temporal Coarse-to-Fine and Spatial Aggregation strategies, but it does not produce 360 ◦ × 180 ◦ FoV panoramas. 360DVD [39] trains a 360-Adapter to exploit the generative capabilities of text-to-video diffusion models, achieving text-driven 360-degree video generation. Most relevant to our work, Imagine360 [33] utilizes a dual-branch design, similar to [46], to synthesize panoramic videos from given perspective inputs. However, the equirectangular videos generated by both 360DVD [39] and Imagine360 [33] exhibit severe distortion, especially at the poles, significantly degrading the sense of immersion and realism. We argue that the spatial distortion and motion drifting caused by ERP make it difficult for models to effectively understand the polar regions with limited training data. After all, generating perspective videos with extreme motion is still a challenge, let alone panoramas.

Current video generation models employ 3D positional encodings, enabling them to uniformly model videos across both spatial and temporal dimensions. Building on this foundation, open-source video models, such as Wan 2.1 [35] and CogVideoX [43], have acquired rich generative capabilities by training on extensive datasets of perspective videos. Multi-view generation approaches represent a panoramic video as a collection of multiple perspective videos. Although these perspective views align well with the native generative capabilities of video models, the spatial relationships between them are only inferred implicitly. This is typically achieved by introducing extra positional encodings or by constructing partial overlaps between adjacent faces. Such methods, however, tend to disrupt the model's inherent generative abilities and introduce artifacts like color discrepancies and visible seams in the resulting panoramic videos. Consequently, they fail to meet the demand for high-quality panoramic video generation. In contrast, an intact panoramic representation can naturally align with the native 3D attention mechanism of DiTs. In this paradigm, the model is able to capture global information across the entire panoramic scene, leading to the generation of superior panoramic videos. Our proposed ViewPoint Map is precisely such an explicit panoramic representation. It projects the non-Euclidean spherical data onto a single, unified Euclidean plane. This provides global continuity and allows the model to "see" the entire spherical space in a single pass.

Video Outpainting. Formally, extending from a given perspective view to a panoramic video is a kind of video outpainting task. Benefiting from pretrained diffusion models [10, 31, 29, 9], previous works [36, 5] have made notable progress in perspective video outpainting. However, due to the scarcity of 360-degree video data and the modality gap introduced by its unique representation form, panoramic video outpainting remains an open question. In this paper, we design a novel panorama

representation format to fully exploit the generative priors of video diffusion models, aiming to address the absence of high-quality panoramic video outpainting.

3 Method

3.1 Preliminary

Latent diffusion models [29, 10, 26] conduct a series of diffusion and denoising processes in latent space. Given a clean latent code x 0 from the training data, a noisy latent x t is obtained by adding an random noise x 0 ∼ N (0 , I ) to x 0 according to a timestep t ∈ [0 , 1] . Following the flow matching paradigm [17, 7], x t is defined as:

The training objective of the model is to predict the velocity v t , thus, the loss function of the training process can be formulated as:

where c txt is the text condition, θ is the model weights, u is the predicted velocity and v t is the ground truth velocity.

add noise

concat

remap

ViewPoint Map

Input

Projected Input Perspective Video

Mask

Pano Attention

Perspective Attention

L

F

B

R

Prompt

×

N

Output

Attn

Block

L

F

B

R

Attn

Block

Equirectangular Map

CubeMap

F

F

F

project

r    r*

project

a

√2 a

r

r*

3.2 ViewPoint Map

To maintain spatial continuity while reducing spatial distortion, we propose ViewPoint Map, a novel panorama representation that combines the advantages of ERP and CP. We first sample six cube faces from either an equirectangular map or a cubemap, namely F , R , B , L , U , and D , which represent the front, right, back, left, up, and down faces, respectively. The four faces in the horizontal view, i.e., F , R , B , L , typically contain most of the visual content that people focus on. Therefore, we construct pseudo-perspective subregions centered around these four faces. As shown in the first row

of Fig. 2, we select one face as the central face (assuming the side length of a ), then diagonally dividing each of the four adjacent faces into four parts, and finally, the central face and its four adjacent parts are concatenated to form a square region with a side length of √ 2 a . This design makes the central face more spatially continuous with the four adjacent faces. For example, if F is the central face, then its four adjacent faces are L , R , U and D , located to the left, right, above, and below L , respectively. After obtaining the four subregions, we perform rotation and concatenation on them to further ensure that the center of the ViewPoint Map-the D face-is also continuous. To address the splitting of U , we project the semicircular region on the U face adjacent to the central face into an equilateral right triangle. Formulally, for any point P ( r, θ ) on the semicircular region, where r ∈ [0 , a ] and θ ∈ [0 , π ] , with a being the diameter of the semicircle. The scale ratio is defined as:

then the scaled radial coordinate r ∗ is computed by:

This design allows the U faces of the four subregions to have a small overlapping portion, thereby ensuring internal consistency of the entire U face.

3.3 Pano-Perspective Attention

ViewPoint Map offers global panorama information and effectively utilizes the inherent in-context generation capabilities [11] of diffusion models. To further accelerate convergence and exploit the priors of the base model, we propose a Pano-Perspective Attention mechanism. Specifically, PanoAttention is responsible for contextual learning to maintain spatial consistency, while PerspectiveAttention focuses on generating fine-grained visual information for each subregion. As shown in the second row of Fig. 2, we first concatenate the noisy Viewpoint Map, the projected input perspective video, and the associated mask along the channel dimension in the latent space. The shape of the concatenated features is ( batch _ size, channels, frames, height, width ) . After passing through the Pano Attention block, we reshape it to (4 × batch _ size, channels, frames, height/ 2 , width/ 2) and feed it into the Perspective Attention for per-regional modeling. Each attention block consists of a self-attention layer, a cross-attention layer, and an FFN, the input prompt is integrated through the cross-attention mechanism.

3.4 Overlapping Fusion

Each subregion in a ViewPoint map has a partial spatial overlap with the two adjacent subregions. To achieve smoother transitions between subregions, we propose a gradient fusion mechanism. As shown in Fig. 3, the overlapping parts between two subregions form a regular rhombus, therefore we construct an overlapping fusion weight W ∈ R n × n where the values decay from the area near the central face to the edges.

Subregion

L

Subregion

F

Subregion

R

Subregion

B

For each subregion S d ∈ R r × r , r = 2 n , d ∈ [ L , F , R , B ] , the overlapping fusion process can be described by the following formulas:

where i, j denote the row and column indices, respectively. Next, we define rotation. For a given matrix A , we have:

where J is the exchange matrix. Finally, for each subregion, we have:

Note that we apply overlapping fusion to each subregion simultaneously, rather than in the order specified in the above formulas.

For the U face, we first project the triangles back into semicircles and transform the rhombus into a petal shape formed by the overlap of two circles. Finally, we use a similar fusion mechanism to fuse the overlapping of the four semicircles.

4 Experiments

4.1 Implementation Details

Datasets & Preprocess. Our model is trained on 4 panorama datasets, including one image dataset, Flickr360 [3], and three video datasets, WEB360 [39], ODV360 [3], and 360+x [4]. Among them, only WEB360 [39] comes with captions; therefore, we use Qwen-VL [2] to annotate the remaining three datasets, generating corresponding descriptive captions. All datasets are resized to a resolution of 512 × 1024 , and during training, video data is divided into clips of 49 frames each.

Training. We first inflate the patch embedding layer of the powerful video generation model, Wan2.1 [35], from 16 to 33 channels to accommodate the input data, and then fine-tune the entire model. The training process is executed on 8 × NVIDIA A100 GPUs, using a batch size of 1 and a learning rate of 1 e -4 . We employ a joint image-video training strategy, treating images as videos with only one frame.

4.2 Qualitative Comparison

We compare our approach with three previous methods, including one perspective video outpainting method, Follow-Your-Canvas [5], and two 360-degree video generation methods, 360DVD [39] and Imagine360 [33]. Adopting equirectangular representation, both 360DVD [39] and Imagine360 [33] exhibit severe distortion at the poles. As shown in Fig. 4, the sky appears to have "black holes", while the ground shows "swirls". We argue that these artifacts are caused by the model's difficulty in handling the spatial-temporal distortion introduced by ERP, as a slight movement can result in significant disturbances at the poles.

The visualization of the comparison results is shown Fig. 5, where we present the ERP format and perspective views in four horizontal directions, with arrows indicating the flow of time. As shown in Fig. 5, 360DVD [39] only recognizes textual input. The generated frames show limited

360DVD

Imagine360

Ours

visual quality and exhibit minimal motion. Follow-Your-Canvas (F-Y-C) [5] generates satisfactory perspective videos; however, its results suffer from severe spatial inconsistency, especially on the B face, exhibiting a noticeable sense of disconnection. Although Imagine360 [33] synthesizes reasonable panoramic videos, it lacks spatial-temporal continuity. As in the first example, the little girl is running, but the ground does not move accordingly and shows a noticeable texture difference from the input video. In the second example, the aircraft in the input video is in a dark and tense atmosphere, while the generated panoramic video shows blue skies and white clouds, creating a peaceful scene. Additionally, the aircraft does not integrate well with the surrounding environment, resulting in a discordant effect. In contrast, our approach demonstrates excellent spatial-temporal consistency and supports both textual prompts and input videos, thus enhancing the flexibility of generation. Furthermore, ViewPoint exhibits high motion dynamics, as each perspective view responds to the motion trends of the input video correctly. As in the first case, the input video depicts a little girl running forward, with the prompt describing a magical forest scene at night. Our generated video aligns with both the input video and the prompt, showcasing a strong capability for condition awareness and integration. Similarly, in the second example, the generated panoramic video also integrates with the input video and exhibits high-quality dynamics.

4.3 Quantitative Comparison

In this section, we provide a quantitative comparison of our approach with previous methods. VBench [12] is a comprehensive benchmark suite for video generative models which scores a video in 16 dimensions. We evaluate our approach and previous methods on the ODV360 [3] dataset across five dimensions: "subject consistency", "imaging quality", "motion smoothness" and "dynamic degree". Specifically, we first project the generated videos into six perspective views with a FoV of 90 ◦ , similar to F , R , B , L , U , and D . Then, we apply VBench [12] evaluation in a perspective manner for all projected videos.

The results of the quantitative evaluation are shown in Tab. 1, where our approach achieves the best scores across four metrics. Although 360DVD [39] performs the second best in "subject consistency", it scores the lowest in "dynamic degree", indicating that the videos generated by 360DVD have very little motion. The nearly static videos result in 360DVD achieving a high score in "motion smoothness"; however, such videos fail to meet the demands of generating high-quality panoramic videos. Follow-Your-Canvas [5], on the other hand, is capable of generating high-quality perspective results, but the projected videos exhibit severe spatial distortion, resulting in poor performance in evaluations. In contrast, our approach demonstrates state-of-the-art performance in all four metrics, proving that our method can generate highly dynamic and temporally coherent panoramic videos.

t

t

t

t

t

t

t

t

360DVD

F-Y-C

Imagine360

Ours

A magical forest bathed in the deep colors of night, where

lush, green trees stretch towards the starry sky, their leaves

whispering gently in the cool night breeze.

Input video:

Input video:

L

B

R

F

L

B

R

F

An aerial scene of dark cloud layers, with an airplane

traversing the sky, amid thunder and lightning, an apocalyptic

scenario, surrealism.

4.4 Ablation Study

To demonstrate the effectiveness of our method, we conduct ablation experiments on panorama representation formats and network designs. For the ERP format, we directly fine-tune the base model to adapt to this panoramic representation. For the CP format, we train two models, CP-ICLoRA and CP-4DRoPE, separately to assess the impact of different cubeface-encodings. Technically, CPICLoRA rearranges the six cube faces into a 2-row by 3-column rectangle in raster order, i.e., F , R , B on the first row, and L , U , D on the second row, while CP-4DRoPE assigns an independent positional encoding to each cube face. We also examine the design of Pano-Perspective attention by replacing all Perspective blocks with the intact Pano blocks.

As shown in Fig. 6, ERP exhibits significant artifacts due to the inherent modality gap with the pretrained model. Both cubemap representation methods suffer from spatial discontinuity, wherein CP-4DRoPE shows inconsistency in color tone, while CP-ICLoRA, although achieving better image quality, presents significant spatial fractures. Our full method, in contrast, is capable of generating high-quality and coherent panoramic videos that are superior to other alternative designs in both spatial and temporal aspects.

ERP

CP-4DRoPE

CP-ICLoRA

w/o Pers-Attn

Full Method

Input

'

The scenery by

the lake, with

swaying leaves,

calm water surface,

swaying flowers,

and dreamlike

fairy tale scenes.

'

The results of quantitative ablation are shown in Tab. 2, our full method outperforms the other four alternative designs across four metrics. Since we conduct evaluations in a perspective manner, CPICLoRA performs best in "imaging quality". However, as shown in Fig. 6, though each perspective video shows good visual quality, there is still a discontinuity issue at the boundaries of the cube faces.

To further validate the effectiveness of our design, we conduct a user evaluation with 50 participants to compare different designs across four dimensions: "Spatial Continuity", "Temporal Quality", "Aesthetic Preference" and "Condition Alignment" (detailed in section 4.5). As shown in Tab. 3, the results demonstrate a significant user preference for our method over the alternatives.

4.5 User Study

Despite achieving advanced scores on VBench [12], we conduct user studies, introducing subjective user ratings to further validate the superiority of our approach. We ask participants to vote on the tested videos based on four dimensions: "Spatial Continuity", "Temporal Quality", "Aesthetic Preference" and "Condition Alignment". "Spatial continuity" represents the coherence of scenes and structures in panoramic space, while "Temporal Quality" is used to evaluate motion effects; for example, being nearly motionless and flickering are considered poor performance. "Aesthetic Preference" represents participants' subjective preferences, while "Condition Alignment" refers to the degree to which the generated panoramic video aligns with the input conditions, such as text or video.

Finally, we collect 50 valid questionnaires, each containing 14 sets of videos for comparison, and the results are shown in Fig. 7. Overall, Imagine360 [33] is the second choice among participants, while 360DVD [39] and Follow-Your-Canvas [5], receive the fewest votes. Our approach receives the highest number of votes across all four dimensions, surpassing 360DVD [39], Follow-Your-Canvas [5], and Imagine360 [33], demonstrating our method's ability to generate satisfactory panoramic results.

5 Conclusion

In this work, we present ViewPoint, a novel framework for representing and generating panoramic videos leveraging modern generative models. Specifically, we design a novel representation distinct from traditional panoramic image and video representations, which has the advantage of good spatial continuity and temporal consistency. Through our proposed PanoPerspective attention mechanism, the pretrained model perceives the global spatial structure information of panoramic videos while modeling fine-grained visual features, effectively improving the quality of the generated videos. To further enhance spatial consistency, we propose an overlapping gradient fusion mechanism that fully utilizes the spatial continuity of each subregion, thereby improving the spatial quality of

Spatial Continuity

Temporal Quality

Condition Alignment

360DVD

Follow-Your-Canvas

0

5

10

15

20

25

Imagine360

Ours

panoramic videos. Extensive qualitative and quantitative experiments, as well as user studies, demonstrate the effectiveness of our method, and we believe ViewPoint can provide valuable insights for the omnidirectional vision community.

6 Limitations and Future Work

A significant challenge in panoramic video generation is achieving high resolution. Immersive applications such as virtual reality (VR) necessitate ultra-high resolutions (e.g., 8K, 16K) to deliver a realistic and compelling user experience. However, current pre-trained diffusion models are computationally constrained and cannot directly synthesize content at such scales. Consequently, in line with established practices, our work employs a two-stage generate-then-upscale pipeline to mitigate this limitation.

Looking ahead, our future work will focus on two primary directions. First, we aim to explore end-to-end models for ultra-high-resolution panoramic generation. Second, we will investigate the extension to long-form video generation, for instance, by leveraging autoregressive frameworks.

Acknowledgments

This work is supported by the National Natural Science Foundation of China (NSFC) under Grants 62576328, 62306295, 62225207 and 62436008.

References

Hao Ai, Zidong Cao, and Lin Wang. A survey of representation learning, optimization strategies, and applications for omnidirectional vision. Int. J. Comput. Vis. , pages 1-40, 2025.

Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond. arXiv preprint arXiv:2308.12966 , 2023.

Mingdeng Cao, Chong Mou, Fanghua Yu, Xintao Wang, Yinqiang Zheng, Jian Zhang, Chao Dong, Gen Li, Ying Shan, Radu Timofte, et al. Ntire 2023 challenge on 360deg omnidirectional image and video super-resolution: Datasets, methods and results. In IEEE Conf. Comput. Vis. Pattern Recog. Worksh. , pages 1731-1745, 2023.

Hao Chen, Yuqi Hou, Chenyuan Qu, Irene Testini, Xiaohan Hong, and Jianbo Jiao. 360+x: A panoptic multi-modal scene understanding dataset. In IEEE Conf. Comput. Vis. Pattern Recog. , 2024.

Qihua Chen, Yue Ma, Hongfa Wang, Junkun Yuan, Wenzhe Zhao, Qi Tian, Hongmei Wang, Shaobo Min, Qifeng Chen, and Wei Liu. Follow-your-canvas: Higher-resolution video outpainting with extensive content generation. arXiv preprint arXiv:2409.01055 , 2024.

Zhaoxi Chen, Guangcong Wang, and Ziwei Liu. Text2light: Zero-shot text-driven hdr panorama generation. ACM Trans. Graph. , 41(6):1-16, 2022.

Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Int. Conf. Mach. Learn. , 2024.

Mengyang Feng, Jinlin Liu, Miaomiao Cui, and Xuansong Xie. Diffusion360: Seamless 360 degree panoramic image generation based on diffusion models. arXiv preprint arXiv:2311.13141 , 2023.

Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. Int. Conf. Learn. Represent. , 2024.

Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Adv. Neural Inform. Process. Syst. , 33:6840-6851, 2020.

Lianghua Huang, Wei Wang, Zhi-Fan Wu, Yupeng Shi, Huanzhang Dou, Chen Liang, Yutong Feng, Yu Liu, and Jingren Zhou. In-context lora for diffusion transformers. arXiv preprint arxiv:2410.23775 , 2024.

Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, Yaohui Wang, Xinyuan Chen, Limin Wang, Dahua Lin, Yu Qiao, and Ziwei Liu. VBench: Comprehensive benchmark suite for video generative models. In IEEE Conf. Comput. Vis. Pattern Recog. , 2024.

Nikolai Kalischek, Michael Oechsle, Fabian Manhardt, Philipp Henzler, Konrad Schindler, and Federico Tombari. Cubediff: Repurposing diffusion-based image models for panorama generation. In Int. Conf. Learn. Represent. , 2025.

Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 , 2013.

Jialu Li and Mohit Bansal. Panogen: Text-conditioned panoramic environment generation for vision-andlanguage navigation. Adv. Neural Inform. Process. Syst. , 36:21878-21894, 2023.

Renjie Li, Panwang Pan, Bangbang Yang, Dejia Xu, Shijie Zhou, Xuanyang Zhang, Zeming Li, Achuta Kadambi, Zhangyang Wang, Zhengzhong Tu, et al. 4k4dgen: Panoramic 4d generation at 4k resolution. 2024.

Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le. Flow matching for generative modeling. In Int. Conf. Learn. Represent. , 2023.

Aoming Liu, Zhong Li, Zhang Chen, Nannan Li, Yi Xu, and Bryan A Plummer. Panofree: Tuning-free holistic multi-view image generation with cross-view self-guidance. In Eur. Conf. Comput. Vis. , pages 146-164. Springer, 2024.

Zhiheng Liu, Ruili Feng, Kai Zhu, Yifei Zhang, Kecheng Zheng, Yu Liu, Deli Zhao, Jingren Zhou, and Yang Cao. Cones: Concept neurons in diffusion models for customized generation. arXiv preprint arXiv:2303.05125 , 2023.

Zhiheng Liu, Yifei Zhang, Yujun Shen, Kecheng Zheng, Kai Zhu, Ruili Feng, Yu Liu, Deli Zhao, Jingren Zhou, and Yang Cao. Customizable image synthesis with multiple subjects. Advances in neural information processing systems , 36:57500-57519, 2023.

Zhiheng Liu, Ka Leong Cheng, Qiuyu Wang, Shuzhe Wang, Hao Ouyang, Bin Tan, Kai Zhu, Yujun Shen, Qifeng Chen, and Ping Luo. Depthlab: From partial to complete. arXiv preprint arXiv:2412.18153 , 2024.

Zhiheng Liu, Hao Ouyang, Qiuyu Wang, Ka Leong Cheng, Jie Xiao, Kai Zhu, Nan Xue, Yu Liu, Yujun Shen, and Yang Cao. Infusion: Inpainting 3d gaussians via learning depth completion from diffusion prior. arXiv preprint arXiv:2404.11613 , 2024.

Zhiheng Liu, Ka Leong Cheng, Xi Chen, Jie Xiao, Hao Ouyang, Kai Zhu, Yu Liu, Yujun Shen, Qifeng Chen, and Ping Luo. Manganinja: Line art colorization with precise reference following. In Proceedings of the Computer Vision and Pattern Recognition Conference , pages 5666-5677, 2025.

Zhiheng Liu, Xueqing Deng, Shoufa Chen, Angtian Wang, Qiushan Guo, Mingfei Han, Zeyue Xue, Mengzhao Chen, Ping Luo, and Linjie Yang. Worldweaver: Generating long-horizon video worlds via rich perception. arXiv preprint arXiv:2508.15720 , 2025.

Jingwei Ma, Erika Lu, Roni Paiss, Shiran Zada, Aleksander Holynski, Tali Dekel, Brian Curless, Michael Rubinstein, and Forrester Cole. Vidpanos: Generative panoramic videos from casual panning videos. In SIGGRAPH Asia 2024 Conference Papers , pages 1-11, 2024.

William Peebles and Saining Xie. Scalable diffusion models with transformers. In Int. Conf. Comput. Vis. , pages 4195-4205, 2023.

Junxiang Qiu, Lin Liu, Shuo Wang, Jinda Lu, Kezhou Chen, and Yanbin Hao. Accelerating diffusion transformer via gradient-optimized cache. arXiv preprint arXiv:2503.05156 , 2025.

Junxiang Qiu, Shuo Wang, Jinda Lu, Lin Liu, Houcheng Jiang, Xingyu Zhu, and Yanbin Hao. Accelerating diffusion transformer via error-optimized cache. arXiv preprint arXiv:2501.19243 , 2025.

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In IEEE Conf. Comput. Vis. Pattern Recog. , pages 1068410695, 2022.

Ka Chun Shum, Hong-Wing Pang, Binh-Son Hua, Duc Thanh Nguyen, and Sai-Kit Yeung. Conditional 360-degree image synthesis for immersive indoor scene decoration. In Int. Conf. Comput. Vis. , pages 4478-4488, 2023.

Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 , 2020.

Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In Int. Conf. Learn. Represent. , 2021.

Jing Tan, Shuai Yang, Tong Wu, Jingwen He, Yuwei Guo, Ziwei Liu, and Dahua Lin. Imagine360: Immersive 360 video generation from perspective anchor. arXiv preprint arXiv:2412.03552 , 2024.

Shitao Tang, Fuyang Zhang, Jiacheng Chen, Peng Wang, and Yasutaka Furukawa. Mvdiffusion: Enabling holistic multi-view image generation with correspondence-aware diffusion. 2023.

Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, Jianyuan Zeng, Jiayu Wang, Jingfeng Zhang, Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng, Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang, Tianyi Gui, Tingyu Weng, Tong Shen, Wei Lin, Wei Wang, Wei Wang, Wenmeng Zhou, Wente Wang, Wenting Shen, Wenyuan Yu, Xianzhong Shi, Xiaoming Huang, Xin Xu, Yan Kou, Yangyu Lv, Yifei Li, Yijing Liu, Yiming Wang, Yingya Zhang, Yitong Huang, Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng, Yuntao Hong, Yupeng Shi, Yutong Feng, Zeyinzi Jiang, Zhen Han, Zhi-Fan Wu, and Ziyu Liu. Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503.20314 , 2025.

Fu-Yun Wang, Xiaoshi Wu, Zhaoyang Huang, Xiaoyu Shi, Dazhong Shen, Guanglu Song, Yu Liu, and Hongsheng Li. Be-your-outpainter: Mastering video outpainting through input-specific adaptation. In Eur. Conf. Comput. Vis. , pages 153-168. Springer, 2024.

Hai Wang, Xiaoyu Xiang, Yuchen Fan, and Jing-Hao Xue. Customizing 360-degree panoramas through text-to-image diffusion models. In IEEE Winter Conf. Appl. Comput. Vis. , pages 4933-4943, 2024.

Jionghao Wang, Ziyu Chen, Jun Ling, Rong Xie, and Li Song. 360-degree panorama generation from few unregistered nfov images. In ACM Int. Conf. Multimedia , pages 6811-6821, 2023.

Qian Wang, Weiqi Li, Chong Mou, Xinhua Cheng, and Jian Zhang. 360dvd: Controllable panorama video generation with 360-degree video diffusion model. In IEEE Conf. Comput. Vis. Pattern Recog. , pages 6913-6923, 2024.

Tianhao Wu, Chuanxia Zheng, and Tat-Jen Cham. Panodiffusion: 360-degree panorama outpainting via diffusion. In Int. Conf. Learn. Represent. , 2023.

Yuhang Yang, Wei Zhai, Hongchen Luo, Yang Cao, and Zheng-Jun Zha. Lemon: Learning 3d human-object interaction relation from 2d images. In IEEE Conf. Comput. Vis. Pattern Recog. , pages 16284-16295, 2024.

Yuhang Yang, Wei Zhai, Chengfeng Wang, Chengjun Yu, Yang Cao, and Zheng-Jun Zha. Egochoir: Capturing 3d human-object interaction regions from egocentric views. arXiv preprint arXiv:2405.13659 , 2024.

Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv preprint arXiv:2408.06072 , 2024.

Weicai Ye, Chenhao Ji, Zheng Chen, Junyao Gao, Xiaoshui Huang, Song-Hai Zhang, Wanli Ouyang, Tong He, Cairong Zhao, and Guofeng Zhang. Diffpano: Scalable and consistent text to panorama generation with spherical epipolar-aware diffusion. 2024.

Wei Zhai, Yang Cao, Jing Zhang, Haiyong Xie, Dacheng Tao, and Zheng-Jun Zha. On exploring multiplicity of primitives and attributes for texture recognition in the wild. IEEE Trans. Pattern Anal. Mach. Intell. , 46 (1):403-420, 2024. doi: 10.1109/TPAMI.2023.3325230.

Cheng Zhang, Qianyi Wu, Camilo Cruz Gambardella, Xiaoshui Huang, Dinh Phung, Wanli Ouyang, and Jianfei Cai. Taming stable diffusion for text to 360 ◦ panorama image generation. In IEEE Conf. Comput. Vis. Pattern Recog. , 2024.

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Appendix

In this appendix, we discuss the usage of computational resources, including VRAM consumption and inference latency in section 7. In section 8, we demonstrate that our method can support text-driven panoramic video generation, further expanding its application scenarios. Afterwards, we present the limitations of our method in section 9. Finally, we discuss the potential societal impact in section 10.

7 Computational Resources

7.1 Representation Comparison

We compare our method with two mainstream panorama representations in terms of area. As shown in Fig. 8, assuming that the side length of a perspective anchor with a field-of-view (FoV) of 90°×90° is r, the area of the equirectangular representation is 8r², and the area of the 2D grid in the cubemap is 12r², among which the valid representation area is 6r². In contrast, our representation occupies an area of 8r².

r

Equirectangular

Cubemap

ViewPoint

2r

4r

4r

3r

2

√2

r

2

√2

r

7.2 Methods Comparison

Wecompare our method with 360DVD [38], Follow-Your-Canvas [5], and Imagine360 [33] in terms of model complexity and inference-time latency on a single NVIDIA A800(80GB). As shown in Tab. 4, in the case of generating 512 × 1024 49-frame equirectangular videos (with the exception of 360DVD, which is limited to 16-frame video generation), our method demonstrates superior performance in terms of inference latency, model parameters, and peak VRAM consumption compared to prior approaches. Note that we follow the original settings of the compared methods. Among them, Follow-Your-Canvas [5] exhibits extremely long inference latency due to the progressive outpainting design. Imagine360 [33], on the other hand, shows significantly high model complexity and a large number of parameters, resulting from its dual-branch architecture. In contrast, our method achieves excellent model efficiency, generating high-quality results with significantly fewer computational resources.

8 More Application

Our model not only supports video input, but also pure text input. Therefore, our method can also be applied to text-driven panoramic video generation. As shown in Fig. 9, our approach is capable of generating high-quality panoramic video using only textual guidance.

'

A beautiful beach .

'

A scenic view of a river and a forest.

'

'

Snow mountains.

'

'

Night scene, moonlight, starry sky.

'

9 Limitations

As shown in Fig. 10, the panoramic videos generated by our model contain watermarks, exhibit mosaic artifacts on human faces, and occasionally reveal photographic equipment such as selfie sticks in the D regions of the video, which can degrade the immersive experience. These limitations can be attributed to the presence of watermarks and specialized post-processing in our training data [4, 39]. Specifically, 360 camera manufacturers typically embed their logos into the recorded videos, leading to an abundance of watermarked content within the dataset. In addition, some works [4] choose to apply a mosaic effect to human faces in the datasets for ethical and moral considerations. We acknowledge and respect the ethical considerations taken by the creators of the datasets. We argue that these limitations can be addressed by using higher-quality datasets, where high quality refers to, for example, removing watermarks or hiding photographic equipment through algorithms, as well as filtering out video clips containing human faces.

10 Potential Societal Impact

Panoramic video generation has the potential to bring significant positive societal impact. It can empower non-expert users to create high-quality immersive content, thereby fostering innovation in digital art, virtual tourism, education, and cultural heritage preservation. This technology also enhance remote experiences and contributes to accessibility, enabling broader participation in virtual environments for individuals with physical or economic limitations. Furthermore, it offers valuable support in scientific visualization and educational applications, especially in fields such as geography and urban planning. However, generating virtual panoramas also pose several challenges. Concerns around privacy and ethics arise when real-world data containing personal identities are used for training. The potential for misuse, including the creation of deepfakes and misleading visual content, raises serious issues regarding misinformation and public trust. Additionally, copyright infringement

and labor displacement in creative industries are growing concerns. To mitigate these risks, it is essential to implement robust regulatory frameworks, ethical guidelines, and public awareness campaigns that promote responsible use and transparency in AI-driven content generation.

Mosaic effect

Watermarks

Photographic equipment

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a method for adapting a text-to-video diffusion model to upconvert narrow field-of-view videos into seamless 360 panoramic videos. The two main contributions are a parameterization of the panorama into the square domain of the diffusion model that reduces perspective distortion, and a modification to the diffusion model attention mechanism and optimization that allows seamless blending across the edges of the square domain.\n\nSTRENGTHS AND WEAKNESSES:\nThe method effectively addresses the issue of seamless 360 panorama generation for a video diffusion model. The presented results do not show visible seams or singularities, though there are some quality and resolution issues, possibly due to the base Wan 2.1 model. Existing video-generation models can create 360 videos when prompted with \"360\", however since they do not explicitly handle stitching they tend to show seams and singularities (search for \"veo 3 360 videos\" on youtube). The method itself is simple and reasonable: it effectively unwraps a cubemap projection into a square domain, creating triangular flaps to handle overlap. \n\nWhile I believe that straight generation of 360 videos with an unmodified Wan 2.1 would show seams, it would still have been nice to show some baseline results simply by prompting a Wan 2.1 outpainting model with \"360\" or some variation of that prompt. Given the Veo results, I would expect it would be able to produce at least an approximation of an equirect video. Fig. 6 suggests that there is a domain gap that causes bad performance for ERP, but without some more exploration of the prompt I'm not sure I believe this is true. \n\nThe paper doesn't mention camera stabilization, so I assume the input perspective view is always fed into the \"F\" cubemap panel (Fig. 2). Compared to previous work like VidPanos, the problem being solved here is slightly different: since the camera is not stabilized, there is not an expectation that content outside the current input perspective view is kept consistent across the generation. This feels like a limitation if the method is applied to real captured videos, but if the target is extension for generated videos, why not just generate the entire 360 video in one shot? It would be interesting to see what the method does on the VidPanos dataset, though as the paper states vidpanos is not a full 360 dataset or method. \n\nWhile the perspective and panorama attention modifications are useful for reducing seams, they also feel like a practical limitation as they specialize the model architecture to this particular 360 parameterization. It also raises the question of whether using a modified attention mechanism could solve the issues with the ERP projection, without using the presented projection. It is likely that singularities above and below would still exist, but a wrapping attention mechanism might address seams. \n\nThe language in a few places should be tweaked. It's not necessary to say competing methods \"exhibits terrible image quality\" (Fig. 5 caption), for example; this is in the end subjective and unnecessarily negative.\n\nQUESTIONS:\nCould the method incorporate camera stabilization by adjusting the mask and input perspective view? If not, what is the advantage here over directly generating a 360 video?\n\nCan the ERP results in Fig. 6 be improved with more prompt engineering?\n\nIn 4.1, how is a single image fed into the model through the TAE? Is it just stacked?\n\nI'm not clear on exactly what is happening in 3.4, the overlapping fusion section. Is this basically multidiffusion (Bar-Tal, et al) with some rotation and translation?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper targets the problem of panoramic video generation using a diffusion model. It introduces a novel panorama representation called the ViewPoint Map, which avoids the distortions and inefficiencies of equirectangular (ERP) and cubemap (CP) projections. It also introduces a novel pipeline introducing a Pano-attention block to ensure global consistency, and a Perspective-attention block to maintain local fidelity within each view. Experimental results on well-known benchmarks, along with comparisons to fair baselines, demonstrate the effectiveness of the proposed solution.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n* The paper is very well-written and clearly convey its motivation, underline problem, and current solution limitations. \n* The current submission links to an online demo, where several qualitative experiments show the superior performance of the proposed solution.\n* The current manuscript presents a flawless set of experiments and clearly validate their claims and contributions. They authors compare with current SOTA solutions for video generation, compare with different CP encoding formats, and validate the effectiveness of each proposed attention block, i.e., Perspective-attention and Pano-attention blocks in a complete ablation study.\n\n#  Weaknesses\n* A minor weakness of the current manuscript is a missing section that exploits further challenges and limitations like long-horizon video generation and the impact of the video input/output resolutions.\n\nQUESTIONS:\n* Q1: I’m curious about the main challenges and differences if instead of the proposed ViewPoint Map we directly enlarge the FoV of the perspective views from a cubemap projection. For such a naive approach the ERP distortions are avoided and the overlap between adjacent views are ensured. \n\n* Q2: In Fig. 4, the authors provide qualitative results for 360DVD [31] and Image360 [65]. While this comparison effectively illustrates their points, adding results from the proposed method (even via a mention within the caption with further details in the supplementary material) would emphasize the evaluation and your contributions.\n\n* Q3: I’m curious whether the proposed ViewPoint Map could be extended to other omnidirectional view types, such as catadioptric or fish-eye cameras. Could this representation handle those input modalities, and what would be the main challenges when adapting to them?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work focuses on generating high-quality 360-degree panoramic videos, addressing challenges like spatial distortion and discontinuity in existing methods. ​ It introduces the ViewPoint framework, leveraging a novel panorama representation and Pano-Perspective attention to ensure spatial-temporal consistency and fine-grained visual details. ​ Extensive evaluations show ViewPoint outperforms prior approaches in subject consistency, imaging quality, motion smoothness, and dynamic degree. The results highlight its ability to produce immersive, dynamic, and coherent panoramic videos, advancing the field of Panoramic Video Generation. ​\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- Problem: Panoramic video generation enables immersive and wide-field visual experiences from limited views, benefiting VR/AR, autonomous systems, and surveillance applications.\n\n- Approach: Recording 360° videos requires expensive gear, while most users capture narrow FoV clips, making perspective-to-panoramic generation a valuable direction to explore. Moreover, Panoramic data suffers from distortions and discontinuities, while perspective views align well with pretrained generative models.\n\n- This paper proposes building a ViewPoint map is a novel representation designed to combine the advantages of Equirectangular Projection (ERP) and Cubemap Projection (CP). ​ It improves spatial continuity while reducing distortion, making it suitable for panoramic video generation. \n\nIntegrating Equirectangular Projection (ERP) and Cubemap Projection (CP) marks a key advance in panoramic imaging by addressing spatial representation challenges. ERP causes polar distortions, while CP introduces stitching artifacts due to discontinuous cube faces. - \n- The ViewPoint map offers a hybrid solution that maintains global continuity and reduces local distortion. This analysis explores research papers focused on this concept and related innovations in panoramic representation.\n\n- The representation of ViewPoint map, a novel representation designed to combine the advantages of Equirectangular Projection (ERP) and Cubemap Projection (CP) success improves spatial continuity while reducing distortion as presented in the results in Table 1 generating high-quality panoramic videos with superior motion dynamics and spatial-temporal consistency.\n\n - The experimental section is robust, presenting qualitative results that clearly surpass the baselines in terms of spatial-temporal coherence and visual quality. ​ Quantitative evaluations further strengthen the findings, with the model achieving superior scores across key metrics such as subject consistency, imaging quality, motion smoothness, and dynamic degree. ​ Additionally, the user study complements these results, highlighting higher human ratings for the model in dimensions like spatial continuity, temporal quality, aesthetic preference, and condition alignment, demonstrating its effectiveness and user appeal.\n\nWeaknesses\nExperimental advantage over ERP and CP is limited\nAs far as Table 1 is concerned, a major concern with this work is the inconsistency in the underlying video generation models used for comparison. The proposed method builds upon Wan 2.1, whereas all the baseline methods, including Imagine 360, 360 DVD, and Follow Your Canvas, are built on AnimateDiff V2. This discrepancy undermines the fairness of the evaluation. As reported in VideoBench, Wan 2.1 significantly outperforms AnimateDiff V2, particularly in terms of temporal coherence and motion fidelity. Therefore, it is unclear whether the reported improvements in dynamic content generation and spatio-temporal consistency are due to the proposed contributions, such as the ViewPoint representation map and Pano Perspective attention mechanism, or simply a result of using a more powerful base model. Without controlling for the base architecture through ablations or conducting fair comparisons on a shared backbone, the source of the performance gains remains ambiguous. Moreover, instead of using AnimateDiff-based re-implementations only in Table 2, they should also be used for qualitative comparisons in Figures 4 and 5.\n\nMarginal Quantitative Margins (Table 2):\nTable 2 shows Pano-FID, Pano-Smoothness, and user preference scores, but ViewPoint tops 360DVD and Imagine360 by only 3 to 6 percent on each metric (e.g., Pano-FID 16.7 vs. 17.9; Smoothness 0.423 vs. 0.437), differences that lie well within one standard deviation of the baselines. Without confidence intervals or formal significance tests, such razor-thin margins could be statistical noise rather than true superiority. Considering that ViewPoint requires roughly 10 seconds per frame—an order of magnitude slower than the baselines—the community may prefer the leaner methods if perceptual gains are negligible. The authors should therefore report variance across random seeds and run paired t-tests or bootstrap confidence intervals to objectively validate any SOTA claim. Moreover, careful hyperparameter tuning for the baselines in the ablation study could reduce or eliminate the reported advantage of the proposed method.\n\nLimited Temporal Metrics:\nWhile spatial fidelity is measured, temporal coherence is assessed only with the optical-flow-based “Smoothness” metric. There is no human evaluation or task-based assessment of flicker or potential VR experience quality. I observed visible artifacts and distortions in the videos—for instance, in the space video, the astronaut's hand appears distorted and the motion looks artificial. Additionally, there is no evidence or discussion on how foreground motion would appear at the overlapping regions of the panorama.\n\nQUESTIONS:\nGiven that the reported improvements over 360DVD and Imagine360 in Table 2 are within one standard deviation, can you include variance across random seeds and statistical significance testing (e.g., paired t-tests or confidence intervals) to support your SOTA claims?\n\nWhy were the ablation experimented ERP and CP in table 2 with same baseline (Wan 2.1) only included in the quantitative results (Table 2), and not used in the qualitative comparisons shown in Figures 4 and 5? Would the visual advantage still hold under consistent evaluation?\n\nHow do you evaluate temporal coherence beyond the optical-flow-based Smoothness metric? Have you considered human studies or task-based assessments for motion artifacts and flickering, especially in overlapping regions and dynamic scenes like the space video?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper tackles the task of generating panoramic videos from input perspective videos. The authors introduce a new representation called the ViewPoint map, which captures the full 360° scene using four overlapping, contiguous regions. They adapt a pretrained video diffusion model to this setting by incorporating global attention across the four views and local attention within each view. Experimental results show improvements over several baselines, though the comparisons are not entirely fair.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe idea of single-view videos into panoramic videos is interesting.\nSome of the generated outputs are visually impressive.\nQuantitative results demonstrate improvements over prior methods like 360DVD and Imagine360.\n\nWeaknesses:\nThe ViewPoint map is proposed as an alternative to equirectangular and cubemap formats to reduce pole distortion and preserve continuity, but several generated results (e.g., the jet scene, meadow) still show severe top/bottom distortions, undermining this claim. Additionally, the representation is very similar to existing multi-view approaches [1, 2] that use overlapping perspective crops, raising questions about what ViewPoint offers beyond simpler, established alternatives.\nThe method compares against older baselines built on SD-based video diffusion models, while the proposed approach uses Wan2.1 with a DiT backbone, one of the current state-of-the-art models. This significant difference in base model quality makes the comparisons somewhat unfair, as it’s unclear how much of the improvement is due to the representation and architecture changes, versus the stronger underlying model.\n[1] Tang, S., Zhang, F., Chen, J., Wang, P., & Furukawa, Y. (2023). MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion. ArXiv, abs/2307.01097.\n[2] Xie, K., Sabour, A., Huang, J., Paschalidou, D., Klar, G., Iqbal, U., ... & Zeng, X. (2025). VideoPanda: Video panoramic diffusion with multi-view attention. arXiv preprint arXiv:2504.11389.\n\nQUESTIONS:\n- Given that the Wan2.1 is based on a DiT architecture that uses full self-attn across both spatial and temporal dimensions, can the authors provide some intuition on why adding more limited per-view attention layers performs much better in practice?\n- What are the benefits of using the ViewPoint map as opposed to multiple perspective views with overlaps?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-05"}