ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models
Authors: Zixun Fang
Kai Zhu
Zhiheng Liu
Yu Liu
Wei Zhai
Yang Cao
Zheng-Jun Zha
Venue: NeurIPS 2025 poster ()
Keywords: Diffusion Models
Primary area: deep_learning

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
Zixun Fang!:2 1:2 Kai Zhu
Zhiheng Liu
Yu Liu?
Wei Zhai
Yang Cao
Zheng-Jun Zha
MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China Tong Lab
HKK
Figure I: The generated result. The image at the bottom displays the ViewPoint map generated by model, with the background image showing the concatenated equirectangular map derived from ViewPoint map. The generated panoramic image exhibits excellent spatial consistency, as equirectangular map be seamlessly stitched together. Project page: ViewPoint
Abstract
Panoramic video generation aims to synthesize 360-degree immersive videos, holding significant importance in the fields of VR, world models, and spatial in- telligence. 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, propose novel framework utilizing pretrained perspective video models for generating panoramic videos. Specifically, we design novel panorama rep- resentation named ViewPoint map, which possesses global spatial continuity and fine-grained visual details simultancously. 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. Ex- tensive experiments demonstrate that our method can synthesize highly dynamic
Work done at Tong Lab. Corresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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and spatially consistent panoramic videos, achieving state-of -ftte--rrr performance and surpassing previous methods.
1 Introduction
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, L..., 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.
Imagine you're
Achieving this conversion 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. Unfortu- nately, 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
19 24]. Diffusion models [10] 29 32]
and VAEs [14 predominantly trained on perspective imagery, 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 Cuhemap Projection(CP) format, which unfolds the sphere into six faces (each a 90º 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 2D grid, making it difficult for neural model to capture cross-face consistency. In short, existing representations force a trade-off 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-mentionel 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 fachs-each a distortion-free perspective view-and then merge them into a small number of overlapping along their boundaries, they eliminate the hard seams of standard cuberna while remaining fully compatible with existing 2D diffusion models. On this representation, the proposed Persppeesee 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-attentio 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.
panels. Because these panels share content
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- Penopeeereeetvee attention mechanism, enabling the model to simultaneously maintain global spatial continuity across the entire panorama and significantly enhance the preservation of fine- iine-grained details and motion.
Extensive experiments demonstrate that our method can generate high-quality 360-degree videos and achieve ofte--ofttheart performance, outperforming previous approaches.

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2
Related Works
Panorama Representations. Panoramic images are often projected from spherical space the 21) plane for processing and storage (. Equirectangular Projection (ERP) is the most popular format, which uniformly maps pixels from 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 six cube faces with the FoV of 90º x 90º 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
8
18
46
38
30
[5]
aims to synthesize 360-degree immersive images based on user- user-proviii provided textual or visual clues. It requires the generated images to be seamless in spherical space while maintaining rationality in
[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 recursive outpainting and stitching mechanism. PanFusion [46] proposes dual- branch 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 challenging problem, it requires spatial-temporal consistency across the entire spherical space, as well as costly computational resources.
any perspective anchor. Recent studies [3] 34 40
44
46
38
37
explore panoramic video generation. 4K4DGEN 16 animates given high-resolution panoramic image through user intention. However, the requirement of high- quality panoramic inputs limits the application scenarios, as it struggles 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- -oi-tooviddo diffusion models, achieving text-driven 360-degree video generation. Most relevant to our work, Imagine 360 (333 utilizes dual-branch design, similar to [46), to synthesize panoramic videos from given perspective inputs. However, the equirectangular videos generated by both 360DVD 33) 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.
A few works 39
16
25 33
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 VogVid (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 '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 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 given perspective view to panoramic video is a
kind of video outpainting task. Benefiting from pretrained diffusion models [00
31
29 D. 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 open question. In this paper, design a novel panorama
3

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representation format to fully exploit the generative priors of video diffusion models, aiming to address the absence of high-quality panoramic video outpainting.
Method
3.1
Preliminary
Latent diffusion models 29
10 26
conduct a series of diffusion and denoising processes in latent
space. Given clean latent code zn from the training data, noisy latent x, is obtained by adding random noise æi (0, 7) to zo according to timestep E [0, 1]. Following the flow matching
paradigm 17
7 2t defined
24
tz t)
(1)
The training objective of the model is to predict the velocity et, thus, the loss function of the training process can be formulated as:
L E Br [ Ctzt: t; 0) vell"
(2)
where Crat is the text condition, is the model weights, u is the predicted velocity and er is the ground truth velocity.
CubeMap
project
Project
Equirectangular Map
remap
ViewPoint Map
Pano Attentlon
Perspcctive Attention
Input
Attn
Attn Block
Block
Projected Input Perspective Video
Output
Mask
Prompt
Figure 2: Method overview. The first row illustrates how to construct a Viewpoint map from CubeMap Equirectangular Map. We begin by constructing subregions, using J as example to create perspe-perpeective region centered around it. Subsequently, we combine the four subregions form ViewPoint map. The second row shows the pipeline design: first concatenate the noisy Viewpoint map. input video, and relevant mask along the channels dimension, and then employ a Pano-Perspective attention mechanism learn how maintain global spatial consistency while modeling fine-grained visual information.
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 U4 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-persee pseuuo-perspecttve subregions centered around these four faces. As shown in the first row

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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 square region with side length of 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 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 the ViewPoint Map-the address the splitting of U, project the semicircular region on the U/ face adjacent to the central face into an equilateral right triangle. Formulally, for any point P(r, 0) on the semicircular region, where E [0, a) and E [0, r], with a being the diameter of the semicircle. The scale ratio is defined as:
of Fig. 2
face-is also continuous. To
d(0) sinO + cos0
then the scaled radial coordinate r" is computed by:
d(0)
(4)
This design allows the U faces the four subregions to have a small overlapping portion, thereby ensuring internal consistency of the entire L/ face.
3.3 Pano-Perspective Attention
ViewPoint Map offers global panorama information and effectively utilizes the inherent in-context generation capabilities [1]] of diffusion models. To further accelerate convergence and exploit the priors of the base model, we propose a Perspeeeree Attention mechanism. Specifically, Pano- Attention is responsible for contextual learning to maintain spatial consistency, while Perspective- Attention focuses on generating fine ahaiggr visual information for each subregion. As shown in the second row of Fig. 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 concatenated features is (batch_size channels, framess heighl, width). After passing through the Pano Attention block, we reshape to (4 x batch_size, channels, fromes, height/2 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-attentin layer, and an FFN, the input prompt is integrated through the cross-attention mechanism.
3.4 Overlapping Fusion
Each subregion in ViewPoint map has partial spatial overlap with the two adjacent subregions. To achieve smoother transitions between subregions, we propose a gradient fusion mechanism. As shown Fig. the overlapping parts between two subregions form regular rhombus, therefore we construct an overlapping fusion weight W E R=** where the values decay from the area near the central face the edges.
Subregion
Subregiong
Subregiona
Subregions
Figure Overlapping fusion. The four subregions partially overlap with each other, thus propose a gradient fusion mechanism to interpolate the overlapping areas, thereby enhancing spatial consistency.
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For each subregion Sa E R == 2n, d E [,,,FRR,R.. B). the overlapping fusion process can be described by the following formulas:
i+j-2
Wa = 2(n 1)'
i.j = 1,2, ..2...,,
(5)
where i.j denote the row and column indices, respectively. Next, we define rotation. For given matrix A, we have:
(go A J
(6)
(AA) = J -A", (180 A.
(7)
where J is the exchange matrix. Finally, for each subregion, we have:
SLn, Reo (W) + R Ro (Sp))s=nnnn ( (i.j) E [1, x [n + 1,r]
Staa
(9)
S R Ro (W: Roo (S in. Rolgo (W) (6.7) E [n [1r1] x [1,]]
SR.. Rao (W) + (00on(S))inaae
R (W) (f.j) 6 [1, [m++1r))
SR.,,
(10)
SR.,. RR (W) + 90 (SB) Roo (W), (i.j) [1+] x [1,n]
SF., WW + R-
(1so (i,j) [1,m] x [1,
SF.,
(11)
SP.," R1so Riso(W) +
W. (1.j) E [m 11r] x [n 1,r]
SB., W R (S (SL) Riso (W) (1,)) E [1.n] x [1,n]
SB., =
(12)
[BB.," Riso (W) +
W (t,5) E [1 x [n 1,r)
Note that we apply overlapping fusion to each subregion simultancously, rather than in the order specified in the above formulas.
For the 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 similar fusion mechanism to fuse the overlapping of the four semicircles.
Experiments
4.1
Implementation Details
Datasets & Preprocess. Our model is trained on 4 panorama datasets, including one image dataset, Flickr360 33] and three video datasets, WEB360 [3), 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 resized a resolution of 512 x 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 x NVIDIA A100 GPUs, using batch size of 1 and learning rate of le - 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 Imagine360 [33]. Adopting equirectangular representation, both 360DVD 39] and Imagine360 [3] exhibit severe distortion the poles. As shown in Fig. the sky appears to have "black holes", while the ground shows "swirls". We argue that these artifacts are caused by the model's 's difficulty in handling the spatial-temporal distortion introduced by ERP, slight movement can result in significant disturbances at the poles.
(5), and two 360-degree video generation methods, 360DVD [39 and
The visualization of the comparison results is shown Fig. 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

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Ours imagine360
Despite numerous efforts to mitigate geometric distortion at the poles, both methods still struggle with generating realistic top and bottom views. The distortion at the poles is even more pronounced in video scenes, severely affecting realism and immersion.
Figure 4: Distortion in 360DVD (39 and Imagine360 33
visual quality and exhibit minimal motion. Follow-Your-Cae Your-Canva (F-Y-C) [ generates satisfactory perspective videos; however, its results suffer from severe spatial inconsistency, especially on the B face, exhibiting noticeable sense of disconnection. Although Imagine360 [33] synthesizes reasonable panoramic videos, it lacks opatial-tempont 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 imput video. In the second example, the aircraft in the input video is in 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-tempon 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 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 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 comprehensive benchmark suite for video generative models which scores 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, and DD. Then, we apply VBench [12 evaluation in a perspective manner for all projected videos.
The results of the quantitative evaluation are shown in Tab. 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 high score in "motion smoothness"; however, such videos fail to meet the demands of generating high-quality panoramic videos. Follow-Yone Your-Canvar (5) on the other hand, capable 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-arr performance in all four metries, proving that our method can generate highly dynamic and temporally coherent panoramic videos.
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A magical forest bathed in the deep colors of night, where lush, green trees stretch towards the starry sky, their leaves whispering gently the cool night breeze.
An aerial scene of dark cloud layers, with an airplane traversing the sky, amid thunder and lightning, an apocalyptic scenario, surrealism.
F-Y-C
Imagine360
Ours
Input video:
Input video:
R.
Figure 5: Qualitative comparison of generated videos. 360DVD is a text-driven approach and produces results with limited visual quality while Soew-Your-Canvaa fails generate panoramic videos with reasonable spatial layout. Imagine360 suffers from ppatial-temporal discontinuity, Our approach, on the other hand, can generate high-quality panoramic videos aligned with the given input. Due to page limits, we highly recommend watching the dynamic videos available at ViewPPint
Table 1: Quantitative comparison on VBench. Our method achieves the best performance in all four metries. Although 360DVD obtains the second highest "subject consistency" score due to the nearly static nature of its generated videos, the results fail to meet the demands of generating high-quality panoramic videos. In contrast, our approach performs well in both motion consistency and dynamics.
Method
subject consistency! imaging quality! motion smoothness? dynamic degreet
Follow Your-
0.8284
0.4464
0.8500
36DDVD
0.8633
0.5394
0,5083
Imagine360
33
0.5859
0.9720
0,8148
Vew.Pooit(AAnimateDff
0.5914
0.9786
0.8633
ViewPolut Wam22.1
35
0.8793
0.5927
0.9800
0.9083
4.4 Ablation Study
To demonstrate the effectiveness 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 format, we train two models, CP-ICLoRA Technically, CP- ICLoRA rearranges the six cube faces into a 2-row by 3-column rectangle in raster order, ie.. F, R. on the first and C, 14, D on the second row, while CP-4DRoPE assigns independent positional encoding to each cube face. We also examine the design of Peno--erspeetive attention by replacing all Perspective blocks with the intact Pano blocks.
and CP-4DRoPF, separately to assess the impact of different cubeface-encoding
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 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 other alternative designs in both spatial and temporal aspects.

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"The scenery the lake, with swaying leaves, calm water surface, swaying flowers, and dreamlike fairy tale scenes."
Input
ERP
w/o Pers-Atth Attn CP-4DROPE
CP-ICLORA
Full Method
Figure 6: Ablation different designs. ERP exhibits serious artifacts due to the natural gap in modality. Both Cube representation methods have spatial discontinuity issues. Without Perspective- Attention, it leads to misalignment with the input video. Our full method can generate reasonable and spatially consistent results.
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 ICLoRA A performs best in "imaging quality". However, shown in Fig. [ though each perspective video shows good visual quality, there still discontinuity issue at the boundaries of the cube faces.
perspective manner, CP-
Table 2: Quantitative ablation. Compared to the other four alternative designs, our approach achieves the highest scores across three metrics. Although CP-ICLoRA achieves the highest score in "imaging quality", proving that each of the six faces has good quality independently, the panoramas composed of these six faces exhibit noticeable discontinuities. as indicated by the red boxes in Fig. 6 Therefore, does not meet the demand for high-quality panoramic video generation.
subject consistency! imaging quality? motion smoothness? dynamic degreet
0,8702
0.5396
0.9781
0,8840
CP-4DRoPE
0.8612
0,5104
0.9633
0.88755
CP-ICLoRA
0.8727
0.6018
0,9786
0.8946
w/o Perspective-Atti
0.8663
0.5392
0.9798
Full Method
0.8793
0.5927
0.9800
0,9083
To further validate the effectiveness of our design, we conduct 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.
Table 3: User evaluation. Compared to the other designs, users show significant preference for method across all four dimensions.
Spatial Continuity Temporal Quality Aesthetic Preference Condition Alignment
ERP
3(6%)
3(6%)
2(4%)
4(8%)
CP-4DRoPE
3(6%)
2(4%)
3(6%)
2(4%)
CP-ICLORS
2(4%))
4(8%)
11(22%)
7(14%)
w/o Perspective-Attm
11(2298)
918%)
7(14%)
12(245)
Full Method
31(62%)
32(64%)
27(54%)
25(50%)

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4.5
User Study
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 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.
Despite achieving advanced scores on VBench [[2),
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
[5]. receive the fewest votes, Our approach receives the highest number of votes across all four dimensions, surpassing 360DVD [39,, Follow-Your-CC Cour-Canvas (5), and Imagine360 [33 demonstrating our method's ' ability to generate satisfactory panoramic results.
5 Conclusion
In this work, present ViewPoint, a novel framework for representing and generating panoramic videos leveraging modern generative models. Specifically, we design a novel rep- resentation distinct from traditional panoramic image and video representations, which has the advantage of good spatial continuity and tem- poral consistency. Through our proposed Pano- Perspective attention mechanism, the pretrained model perceives the global spatial structure in- formation of panoramic videos while modeling fine-grained visual features, effectively improv- ing the quality of the generated vidcos. To fur- ther enhance spatial consistency, propose an overlapping gradient fusion mechanism that fully utilizes the spatial continuity of each sub- region, thereby improving the spatial quality of panoramic videos. Extensive qualitative and quantitative experiments, as well as studies, demon- strate the effectiveness of our method, and we believe ViewPoint can provide valuable insights for the omnidirectional vision community.
1620k fallow Your Carnas
Inaglee300
Continsity
Aiggnee
Figure 7: User studies. We ask participants to vote on the videos generated by four methods based on four dimensions, and our approach receives the most votes.
Limitations and Future Work
significant challenge in panoramic video generation achieving high resolution. Immersive applications such as virtual reality (VR) necessitate ultra-high resolutions (e.g.. 8K. 16K) 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-upee uhen-lpe 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.
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Appendix
In this appendix, we discuss the usage of computational resources, including VRAM consumption and inference latency section 7 In section [ 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 Finally, we discuss the potential societal impact in section TO
7 Computational Resources
7.1 Representation Comparison
We compare method with two mainstream panorama representations in terms of area. As shown in Fig. g assuming that the side length perspective anchor with ffeld-of-view (FoV) of 90"x90" 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 81?.
4r
2v2r
4r
2v2r
3r
Equirectangular
Cubemap
ViewPoint
Figure 8: Comparison of different representations terms of area.
7.2 Methods Comparison
33 in terms of We compare our method with 360DVD 38 Follow-Your-Canae [5], and Imagine360
model complexity and inference-ting latency on single NVIDIA A A800(80GB). As shown in Tab. 4 in the case of generating 512 x 1024 9r-frame equirectangular videos (with the exception 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, Hallow-YourCaavvaa exhibits extremely long inference latency due to the progressive outpainting design. Imagine360 33 the other hand, shows significantly high model complexity and large number parameters, resulting from its dual-branch architecture. In contrast, our method achieves excellent model efficiency, generating high-quality results with significantly fewer computational resources.
Table 4: Comparison of computational resource usage with previous methods. Note that 360DVI only supports the generation of 16-frame videos, while the other three methods are evaluated the 49-frame scenario.
360 frames)
Follow-Your Your--aaa-aa
Imagine360
Ours
Latency|
1min30s
29min 19s
4min44s
1min47s
Peak VRAM.
17.44GB
32.57GB
40.45GB
20.77GB
Parameters]
1.33B
1.42B
5.94B
1.42B
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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. b our approach is capable of generating high-quality panoramic video using only textual guidance.
"A beautiful beach.
"A scenic view of river and forest."
"Snow mountains." "
"Night scene, moonlight, starry sky."
Figure 9: Text-driven generation. Our method can generate high-quality panoramic video using only textual guidance.
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 human faces 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 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, 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
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and labor displacement in creative industries are growing concerns. To mitigate these risks, it essential to implement robust regulatory frameworks, ethical guidelines, and public awareness campaigns that promote responsible use and transparency in Al-driven content generation.
Mosaic effect
Watermarks
Photographic equipment
Figure 10: Potential visual artifacts.
23


Reviews:
[{"review_id": "kNkgWk46jc", "reviewer": "Reviewer_WqAv", "summary": "The 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.", "questions": "- 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?", "limitations": "yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer WqAv, we thank you for recognizing the strengths of our work, particularly our novel approach for converting single-view videos into panoramic ones and the method's visually impressive results. We appreciate your valuable comments and questions, and we provide our detailed responses below.\n\n### **Q1: Analysis of Results with Suboptimal Quality at the Poles.**\nAs shown in Fig. 4 in the main paper, compared to previous methods, our proposed approach significantly mitigates distortion at the poles, both subjectively and objectively. Prior works often exhibit severe artifacts like \"black holes\" or \"swirls\" (Fig.4), which drastically undermine the sense of immersion and realism. By contrast, our method may exhibit spatial fluctuations in rare cases, a behavior analogous to that in perspective video generation. (It is worth noting that even in standard perspective scenarios, pre-trained video generation models can suffer from spatial distortion, as this is an issue inherited from the base models themselves). \n\nTo further demonstrate the effectiveness of our method, we re-evaluate the quality of the top and bottom poles of each panoramic video and conduct a user study. The results blow show that our method outperforms other methods. (user preference: we ask 30 participants to rate the video quality at the poles for each method on a score of 1 to 100. The average score is reported as the final result.)  The results demonstrate that our method significantly reduces distortion at the poles compared to previous state-of-the-art methods.\n\n| Method      | subject consistency | imaging quality | motion smoothness | dynamic degree | user preference |\n| ---        |    :----:   |     :----:   |      :----:   |      :----:   |        :----:   |    \n| Follow-Your-Canvas | 0.7935 | 0.3766 | 0.8941| 0.8380 | 8.72 |\n| 360DVD | 0.8018 | 0.4082 | 0.9363 | 0.5026 | 40.86 |\n| Imagine360| 0.8367 | 0.5130 | 0.9381 | 0.7944 | 58.90 |\n| ViewPoint | **0.8529** | **0.5685** | **0.9524** | **0.8821** | **74.34** |\n\n### **Q2: Distinctions and Advantages over Multi-view Generation Methods.**\nOur proposed ViewPoint Map is a unified representation that projects a non-Euclidean panorama into a single, Euclidean 2D square, thereby providing explicit global continuity. This is fundamentally different from multi-view representations, which use multiple isolated perspectives to represent a panorama. In such approaches, the spatial-temporal correlations of the panorama are entirely learned implicitly through subsequent attention mechanisms, making it more challenging to generate high-quality panoramic videos.\n\nMost of the multi-view generation methods (e.g., the methods you mentioned [1, 2]) first project the panorama into multiple overlapping perspective views. They then leverage multi-view attention to learn the relationships between these views, and finally stitch them back together into a panorama. However, these approaches struggle to grasp global continuity and plausibility. For instance, color discrepancies between the individual perspectives can lead to visible seams, or the stitched result might produce incoherent scenarios, such as a room appearing with two doors. \n\nMoreover, multi-view generation methods face a trade-off between the amount of overlaps and computational resources. To ensure better consistency, larger overlapping regions are often required, which in turn necessitates generating more perspective images. For example, the method you cited in [2] projects an ERP panorama into eight perspective views. While the six horizontal views partially overlap with each other, the top and bottom views are completely isolated, having no overlap with their horizontal neighbors. To create overlap for the top and bottom views as well, even more perspective images would be needed, implying a significant increase in computational cost. This is particularly costly because panoramic video generation is an inherently compute-intensive task.\n\nBy contrast, our representation is inherently global and unified. This not only enables the model to enforce true global consistency—going beyond just the relationships between separate views—but also offers superior computational efficiency. As detailed in Table 1 in the supplementary material, our method outperforms all previous methods in latency and VRAM usage.\n\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\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\n### **Q3: Re-implementing ViewPoint on AnimateDiff.**\nFollowing your suggestion, we re-implement our method based on Stable Diffusion v1.5 + AnimateDiff v2 and Stable Diffusion v2.1 + AnimateDiff v2, and the results confirm its superiority over other approaches across all metrics. The evaluation results are presented in the table below. \n| Method | base model | subject consistency | imaging quality | motion smoothness | dynamic degree |\n| ---        |    :------:   |     :----:   |      :----:   |      :----:   |   :----:   |    \n| Follow-Your-Canvas | SD v2.1+AnimateDiff |0.8284 | 0.4464 | 0.9655 | 0.8500 |\n| 360DVD | SD v1.5+AnimateDiff | 0.8633 | 0.5394 | 0.9703 | 0.5083 |\n| Imagine360| SD v2.1+AnimateDiff | 0.8547 | 0.5859 | 0.9720 | 0.8148 |\n| ViewPoint-AnimateDiff-v1.5 | SD v1.5+AnimateDiff  | **0.8669** | 0.5602 | **0.9745** | **0.8597** |\n| ViewPoint-AnimateDiff-v2.1 | SD v2.1+AnimateDiff  | **0.8681** | **0.5914** | **0.9786** | **0.8633** |\n| ViewPoint-Wan2.1 | Wan2.1(1.3B) | **0.8793** | **0.5927** | **0.9800** | **0.9083** |\n\nAlthough the superior generation capabilities of Wan2.1 significantly benefit many downstream tasks, our experiments demonstrate that our method remains effective even with weaker base models, outperforming prior works across four metrics. We have added the above table results to the main comparison in the latest manuscript, and included the visualization results of ViewPoint-AnimateDiff in the supplementary material. Unfortunately, due to the rebuttal policy, we are unable to display the generated videos in any links.\n\n### **Q4: Intuitive Explanation of the Advantages of Per-view Attention Layers.**\nThe modality gap between panoramic data and the pre-training **perspective** data of video generation models makes it difficult for the network to directly model the panorama, resulting in poor performance (as shown in Table 2 in the main paper). Therefore, we propose per-view attention to divide the panoramic representation into multiple **pseudo-perspective** views to align with the pre-training priors and thus fully exploit the generative capabilities of video generation models. \n\nSpecifically, our method cyclically divides the ViewPoint into four pseudo-perspective sub-regions, a data format that better aligns with the pre-trained priors of Wan2.1. Subsequently, these four sub-regions are merged back into the ViewPoint Map, and Wan2.1's 3D attention mechanism is utilized to model global spatio-temporal relationships. This divide-and-merge design enables us to fully leverage the model's generative power (e.g., enhancing dynamism and improving visual quality) while simultaneously strengthening the global continuity of the ViewPoint Map. It is important to note that we are not introducing new layers. Instead, we retain the original attention layers of Wan2.1 and partition them into two functional parts: one part focuses on per-view attention, while the other is dedicated to capturing global continuity."}, {"review_id": "XgCFqwzm9t", "reviewer": "Reviewer_ikfJ", "summary": "This 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. ​", "questions": "Given 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?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\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.", "was_revised": true, "final_justification": "Authors have addressed my concerns regarding fair comparison with the baselines, clarified the temporal evaluations, and acknowledged the artifacts still present in the generated outputs. While the proposed method still has some issues, the work demonstrates sufficient technical novelty and introduces a new representation for approaching the task. Therefore, I am changing my rating to Borderline Accept.", "rebuttal": "We thank the reviewer's comments and questions. However, we are very **confused** by your factually inaccurate statements regarding our paper's quantitative results. You mentioned that\n> \"Table 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)...\"\n\nIn fact, in our Table 2, we use four metrics: ***subject consistency***, ***imaging quality***, ***motion smoothness***, and ***dynamic degree***. The metrics you referred to—***Pano-FID***, ***Pano-Smoothness***, and ***user preference scores***—are **NOT** present anywhere in our paper. Furthermore, the numerical values you cited (i.e., \"Pano-FID ***16.7*** vs. ***17.9***; Smoothness ***0.423*** vs. ***0.437***\") also do **NOT** exist in our manuscript.\n\nRegarding the computational latency, you stated\n> \"Considering that ViewPoint requires roughly ***10*** seconds per frame......\". \n\nHowever, this is inaccurate. As shown in Table 1 in our supplementary material, our method requires only 1 minute and 47 seconds to generate a 49-frame video, which translates to ***2.18*** seconds per frame. In fact, our method is the fastest among all compared approaches.\n\nAlso, we are confused by your comments regarding the lack of human evaluation, specifically when you stated\n> \"There is no human evaluation or task-based assessment of flicker or potential VR experience quality...\" \n>\n> \"Have you considered human studies or task-based assessments for motion artifacts and flickering...\" \n\nIn fact, as detailed in Section 4.5, **User Study**, we conducted a comprehensive human evaluation across four distinct dimensions: ***Spatial Continuity***, ***Temporal Quality***, ***Aesthetic Preference***, and ***Condition Alignment***. We explicitly defined one of these dimensions, ***Temporal Quality***, in the paper as follows (lines 250-251)\n> \"***Temporal Quality*** is used to evaluate motion effects; for example, being nearly motionless and flickering are considered poor performance.\" \n\nThe results of this user study, presented in the main paper, confirmed the effectiveness of our method in all evaluated aspects, including temporal quality.\n\nFor the remaining questions, we provide our responses below.\n## **Q1: Replacing the Baseline.**\nWe re-implement our method on top of Stable Diffusion v1.5 + AnimateDiff v2 and Stable Diffusion v2.1 + AnimateDiff v2. The evaluation results are presented in the table below:\n| Method | base model | subject consistency | imaging quality | motion smoothness | dynamic degree |\n| ---        |    :------:   |     :----:   |      :----:   |      :----:   |   :----:   |    \n| Follow-Your-Canvas | SD v2.1+AnimateDiff |0.8284 | 0.4464 | 0.9655 | 0.8500 |\n| 360DVD | SD v1.5+AnimateDiff | 0.8633 | 0.5394 | 0.9703 | 0.5083 |\n| Imagine360| SD v2.1+AnimateDiff | 0.8547 | 0.5859 | 0.9720 | 0.8148 |\n| ViewPoint-AnimateDiff-v1.5 | SD v1.5+AnimateDiff  | **0.8669** | 0.5602 | **0.9745** | **0.8597** |\n| ViewPoint-AnimateDiff-v2.1 | SD v2.1+AnimateDiff  | **0.8681** | **0.5914** | **0.9786** | **0.8633** |\n| ViewPoint-Wan2.1 | Wan2.1(1.3B) | **0.8793** | **0.5927** | **0.9800** | **0.9083** |\n\nEven with weaker base models, our method outperforms prior works across four metrics. We would love to demonstrate the re-implemented results. Regrettably, the rebuttal guidelines prevent us from sharing an external link for the videos. \n\n## **Q2: Variance of the Random Seed.**\nIn the quantitative evaluation (Table 1 and Table 2), we use random seeds ranging from 42 to 52 for all compared methods, and take the average as the final result. Here, we present the variance in qualitative evaluation.\n| Method | subject consistency | imaging quality | motion smoothness | dynamic degree |\n| ---        |    :----:   |      :----:   |      :----:   |   :----:   |    \n| Follow-Your-Canvas  |0.8284(±0.014) | 0.4464(±0.008) | 0.9655(±0.011) | 0.8500(±0.018) |\n| 360DVD | 0.8633(±0.013) | 0.5394(±0.010) | 0.9703(±0.014) | 0.5083(±0.026) |\n| Imagine360| 0.8547(±0.011) | 0.5859(±0.013) | 0.9720(±0.012) | 0.8148(±0.022) |\n| ViewPoint-AnimateDiff-v1.5 |  **0.8669**(±0.009) | 0.5602(±0.010) | **0.9745**(±0.012) | **0.8597**(±0.016) |\n| ViewPoint-AnimateDiff-v2.1 | **0.8681**(±0.010)  | **0.5914**(±0.009) | **0.9786**(±0.010) | **0.8633**(±0.012) |\n| ViewPoint-Wan2.1 | **0.8793**(±0.011) | **0.5927**(±0.012) | **0.9800**(±0.013) | **0.9083**(±0.013) |"}, {"review_id": "B3OBDULO9i", "reviewer": "Reviewer_C4Si", "summary": "This 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.", "questions": "* 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?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "# 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.", "was_revised": true, "final_justification": "I firmly believe this paper offers sufficient technical novelty and quality for acceptance at NeurIPS 2025: it advances the current state-of-the-art within its scope, introduces original ideas, is reproducible, and presents numerous experiments that objectively validate the results. Please, let me elaborate on these points based on the discussion in this rebuttal.\n\n* Although some concerns were found by reviewer **WqAv** related to unfair evaluations using different architectures and a better backbone model. The authors has shown competitive results using other backbone models (weaker ones compared to Wan2.1), i.e., Stable Diffusion v1.5 + AnimateDiff v2 and Stable Diffusion v2.1 + AnimateDiff v2. This objectively shows that the technical contribution in the proposed solution does not hold due to a strong backbone model, but to the proposed pipeline.\n\n* The proposed solution presents a plausible alternative to ERP (Equirectangular Projection) video generation using something different that Cube-map projection or multiple overlapping perspective views. The authors presented an ablation for this in **Tab.2** (main manuscript), and show strong evidence in the responses to the reviewer **ikfJ**. This shows that the proposed ViewPoint Map is effective and valid.\n\n* The qualitative results presented in the manuscript and in the anonymous link in the paper, show a consistent and coherent video generation in ERP. This shows the effectiveness of the proposed solution qualitatively. Additionally, quantitative results in Table 1 (in the main paper), and experiments in the response to reviewer **WqAv, Q1**, demonstrate the effectiveness of the proposed method specifically for the ERP poles, which are the main pain-points for such imagery format.\n\nTo me, it seems that the responses to reviewer **dty6** have been addressed. The authors present results using other prompts for ERP for The Effect of Prompt Engineering, showing not major difference. Additionally, the authors discuss about camera stability, and more importantly they discuss about details of how the proposed solution differs in the attention mechanism to previous approaches.\n\nFor all these reasons, I keep my rate to (5) Accept.", "rebuttal": "Dear Reviewer C4Si, we sincerely thank you for your recognition of the quality of our results and our proposed ViewPoint representation. We have carefully considered your insightful suggestions and questions, and our responses are as follows:\n\n### **Q1: Further Challenges and Limitations.**\nAs you pointed out, video duration and resolution remain the main challenges in panoramic video generation. There are two core issues: First, the large data volume of high-resolution panoramas (e.g., 8K) imposes significant computational demands that current video generation models struggle to meet. Second, the lack of high-quality panoramic video datasets further exacerbates the problem, fundamentally limiting a model’s ability to produce coherent and richly detailed panoramic content.\nIn response to your suggestion, we have added the following section discussing limitations and future work in the final version.\n\n> ### **Limitations and Future Work**\n> 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.\n>\n> 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.\n\n### **Q2: What would happen if we directly enlarge the FoV of the CubeMap projection?**\nFollowing your suggestion, we enlarge the standard 90° FoV in our CubeMap-4DROPE setup and retrain the models with FoVs set to 92°, 95°, and 100°. The results below demonstrate that enlarging the FoV does not significantly improve the model's performance. As for the visual results, color discrepancies and seams still exist, and the transitions between adjacent faces appear unnatural. (We regret that the rebuttal policy prohibits external links, which prevents us from providing the generated videos.)\n\n| FoV | subject consistency | imaging quality | motion smoothness | dynamic degree |\n| --- | :---: | :---:  | :---:  | :---:  |\n| 90° | 0.8612 | 0.5104 | 0.9633 | 0.8175 |\n| 92° | 0.8639 | 0.5097 | 0.9621 | 0.8203 |\n| 95° | 0.8595 | 0.5088 | 0.9584 | 0.8196 |\n| 100° | 0.8254 | 0.4997 | 0.9501 | 0.8230 |\n\nRegarding the reason for this phenomenon, our analysis is as follows:\nMethods based on CubeMap projection (or multi-view formation) aim to enable the model to implicitly learn the relationships between multiple perspectives. Increasing the FoV of the CubeMap projection to create overlap between faces is an intuitive and reasonable strategy for the panoramic **image** generation task, as it helps the model better recognize the adjacency between faces. However, when we extend the task from panoramic images to panoramic **videos**, this advantage does not directly translate. Our hypothesis is that the temporal dimension in video naturally contains spatial relationship information.\n\nFor instance, consider a person walking left on a road. At time t, this person appears on the Front (F) face. At time t+1, this person appears on the Left (L) face. Even with a 90-degree FoV (no overlap), the model can infer the adjacency of the F and L faces purely from the temporal sequence. The spatial inference capability we expected to gain from a larger FoV is likely already captured by, or redundant to, the information present in the temporal dimension.\n\nAnother drawback is that increasing the FoV leads to higher computational complexity. Here, we provide the formulas for calculating the FoV and the projected area. As panoramas are inherently a spherical representation, assuming the camera is located at the center of the sphere and the distance from the camera to the projection plane (i.e., the equivalent focal length) is ***f***, in the standard CubeMap projection where the horizontal FoV equals the vertical FoV, the side length ***L*** of each face is given as follows:\n$$ L = 2 \\cdot f \\cdot \\tan\\left(\\frac{\\text{FoV}}{2}\\right) $$\nTherefore, we can derive the area ***A*** of each face (i.e., the number of pixels).\n$$ A = (2 \\cdot f \\cdot \\tan\\left(\\frac{\\text{FoV}}{2}\\right) )^2 $$\nRepresenting the spherical view as a CubeMap projection requires six perspectives, so the total area \n***S*** of the entire sphere (i.e., the total number of pixels) is given by:\n$$ S = 6 \\cdot A = 24 \\cdot f^2 \\cdot  \\tan^2\\left(\\frac{\\text{FoV}}{2}\\right) $$\nTo give a concrete example, in a standard setup where each cubeface is 256x256, the FoV is 90 degrees. At this point, the six faces do not overlap, and the total pixel count is 393,216 (approx. 390k pixels).\n\nNow, let's assume we increase the FoV to 95° to create overlap between the faces. The total pixel count rises to 468,302 (approx. 470k pixels). This redundancy is nearly equivalent to adding an entire extra standard CubeMap projection. When we factor in the temporal dimension—for example, a 49-frame video—this requires even more resources. Although the VAE compresses the video in both spatial and temporal dimensions, the overhead from this redundancy is still substantial in a computationally dense task like video generation.\n\n### **Q3: Supplementing Illustrations and Extending Applications.**\n1. Following your suggestion, we have added our visual results to Figure 4 in the final version to better compare the detailed differences between methods.\n\n2. Our representation has the potential to be applied to other modalities, such as those captured by a fisheye camera. We demonstrate this capability in Fig. 1, where we show a ViewPoint Map being converted into a fisheye image. The key challenge when adapting ViewPoint to other omnidirectional view types lies in how to design the processing and padding for the \"void\" regions. For example, if a fisheye camera has an FoV of only 220°, the question is how to handle the remaining 140° region—whether to pad it with zeros or ones, or to use a form of circular padding. This remains an interesting problem worthy of future exploration.\n\nFinally, we again sincerely thank you for your valuable feedback. If you have any further concerns, please do not hesitate to let us know!"}, {"review_id": "QKxJujuOSe", "reviewer": "Reviewer_dty6", "summary": "The 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.", "questions": "Could 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?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "The 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.", "was_revised": true, "final_justification": "I have read the authors' response and comments. Post discussion I am willing to increase my rating given the authors claim to include results on real videos. Please make sure that if the paper is accepted these results are added to the paper.", "rebuttal": "Dear Reviewer dty6, we would like to express our sincere gratitude for your insightful and thoughtful feedback. We are also grateful for your positive comments on the quality of our results. We have carefully considered your questions, and our responses are as follows:\n### **Q1: The Effect of Prompt Engineering.**\nPrompt engineering experiments on the base model (Wan2.1-1.3B) fail to produce ERP-like videos, confirming a significant modality gap with the ERP representation.\n\nIn these experiments, we use phrases like \"a 360 video of ...\" to denote panoramic formats. Due to rebuttal guidelines, we cannot include external links or update the anonymous link in the paper. While we regret being unable to showcase the video results, we will describe the observed phenomena in detail below.\n\nSince Wan2.1 only supports a fixed aspect ratio (e.g., 832x480), which does not conform to the 2:1 aspect ratio of the ERP format, we divide our experiments into two settings:\n\n1. Using the model's native, fixed aspect ratio.\n> Observation: The generated results do not exhibit ERP-like characteristics. Instead, they resemble a bird's-eye perspective. Furthermore, both the visual quality and the dynamism are significantly degraded, with the video being nearly static.\n\n2. Adjusting the aspect ratio to 2:1 (e.g., 960x480) to accommodate the ERP format.\n> Observation: After adjusting the aspect ratio to 2:1, the visual quality improves slightly, but the dynamism remains unchanged, and the output still does not resemble ERP format. Instead, it resembles a wide FoV perspective view, like a landscape wallpaper. (Again, we regret being unable to directly showcase the results.)\n\nRegarding the Veo3 360 videos on YouTube, we have reviewed the content and agree that the results are impressive. However, creators note that they require extensive post-processing using professional software like Premiere.\n\nOur analysis suggests that Wan2.1's failure to generate ERP-like videos stems from a modality gap introduced during pre-training, due to data preprocessing steps like cropping or resizing, which damage the spatial structure ERP data depends on.\n\nTo further investigate the impact of the prompt on the generation results, we design ERP-specific prompts in our ablation study. The results show no significant performance improvement, supporting our claim of a modality gap between Wan2.1 and the ERP format.\n| Method | subject consistency | imaging quality | motion smoothness | dynamic degree |\n| --- | :---: | :---: | :---: | :---: |\n| ERP+\"a 360 video of...\" | 0.8693 | 0.5412 | 0.9770 | 0.8829 |\n| ERP+\"a panoramic video of...\" | 0.8711 | 0.5391 | 0.9776 | 0.8832 |\n| ERP (original) | 0.8702 | 0.5396 | 0.9781 | 0.8840 |\n\n### **Q2: Camera Stabilization and Experiments on the VidPanos Dataset.**\nDuring training, we simulate camera movement by randomly perturbing the Euler angles, which enables our method to generalize flexibly to various input scenarios. This endows our method with a built-in camera stabilization capability, making it applicable to real-world scenarios. Intuitively, this process is equivalent to rotating each frame of the spherical video and then sampling the perspective view from a fixed anchor. We provide the details of the process below:\n\nSince all training data is in ERP format—the most common for panoramic data—a specific preprocessing step is required. We extract a perspective video from the ERP as the condition and convert the full ERP into our ViewPoint Map representation (as shown in Fig. 2). Specifically, the conditional video is obtained through the following steps:\n\n1. We randomly generate a sequence of continuously varying Euler angles, where the length of the sequence is identical to that of the ERP video. The angles are constrained to the following ranges: roll is within (-10°, 10°), pitch is within (-30°, 30°), and yaw is within (0°, 360°). For instance, for a 5-frame video, we would obtain three distinct sequences for the Euler angles, such as: \n***roll***: [0°, 1°, 0°, -1°, -2°]; ***pitch***: [4°, 2°, 1°, -1°, 2°]; ***yaw***: [20°, 23°, 22°, 20°, 18°]\n2. Each set of Euler angles is then applied to its corresponding frame in the ERP video to perform a rotation. That is, the first frame is rotated using the angles {***roll***: 0°, ***pitch***: 4°, ***yaw***: 20°}, the second frame is rotated using {***roll***: 1°, ***pitch***: 2°, ***yaw***: 23°}, and so on.\n3. From this rotated ERP video, we project the conditional perspective video using a fixed anchor (e.g., F). Finally, the entire rotated ERP is converted into a ViewPoint Map for training.\n\nThis simple yet effective data augmentation strategy enables our model to perform camera stabilization without needing to explicitly estimate camera parameters from the input perspective video.\n\nTo validate our design, following your suggestion, we conduct qualitative and quantitative evaluations on **VidPanos-real**. The results confirm the superiority of our method. Although we cannot provide new visualizations in this rebuttal, relevant real-world examples are always available via the anonymous link in our paper. For instance, in the \"red car\" case, while the INPUT VIDEO shows part of the car moving out of frame, the PANO VIDEO retains the full body, supporting our qualitative claims. Quantitative results are shown in the table below.\n\n| Method | subject consistency | imaging quality | motion smoothness | dynamic degree |\n| --- | :---: | :---: | :---: | :---: |\n| Imagine360 | 0.8511 | 0.5694 | 0.9716 | 0.8365 |\n| ViewPoint | 0.8780 | 0.5872 | 0.9729 | 0.9132 |\n\nThe results demonstrate the effectiveness of our method. Unlike Imagine360, which uses external models [1,2] to estimate camera parameters and generate panoramic video, our approach directly embeds camera movement into the model, avoiding potential error accumulation.\n\n[1]A. Veicht, *et al*. GeoCalib: Single-image calibration with geometric optimization. ECCV,2024.\n\n[2]L. Jin, *et al*. Perspective fields for single image camera calibration. CVPR,2023.\n\n### **Q3: Explanation of the Pano-Perspective attention mechanism.**\nAlthough using the ERP representation combined with a well-designed attention mechanism may also help alleviate the seam issue, our approach goes beyond by not only addressing the seam problem but also mitigating the polar distortion and more effectively leveraging the generative capabilities of pre-trained video models, thus enabling the generation of high-quality panoramic videos.\n\nThe method you mentioned can be referenced to Imagine360, which projects ERP into multiple perspectives and employs a cross-domain spherical attention mechanism in an attempt to bridge the modality gap between panoramic data and the pre-training data of video models. While this approach is able to address the seam issue, the polar distortion remains severe (as shown in Figure 4). Moreover, it fails to fully exploit the generative power of existing video models, resulting in lower-quality panoramic video outputs (e.g., limited motion, see Table 1 in the main paper).\n\nIn contrast, our representation, together with the proposed Pano-Pers attention mechanism, enables global structural modeling of panoramic data and further exploits the generative capacity of video models through the construction of pseudo-perspective sub-regions. This capability is not achievable by other representations or attention mechanisms.\n\n### **Q4: Wording and Phrasing.**\nThank you for pointing out our inappropriate wording. \nWe have revised line 192-193, changing the sentence \n> \"The generated frames exhibit terrible image quality and possess little motion.\" \n\nto \n\n> \"The generated frames show limited visual quality and exhibit minimal motion.\" \n\nand updated the caption of Figure 5 from \n> \"360DVD is a text-driven approach and exhibits terrible image quality\" \n\nto \n\n> \"360DVD is a text-driven approach and produces results with limited visual quality\"\n\n### **Q5: How is a single image fed into the model?**\nConsistent with the prevailing training paradigm of video generation models (e.g., Wan2.1), we feed images into the model by treating them as single-frame videos, thanks to the image-video unified encoding VAE. Specifically, a video tensor with a shape of ***[batch, channels(RGB:3), frames, height, width]*** is first passed into the Temporal AutoEncoder of Wan 2.1. Given that the VAE has a temporal-spatial downsampling factor of ***(4, 8, 8)***, the input data must satisfy two conditions: the number of frames must be ***frames = 4t + 1*** (where t is a non-negative integer), and both height and width must be multiples of 8. After being encoded by the VAE, the video is transformed into a latent code with the shape ***[batch, channels(latent:16), t+1, height//8, width//8]***, which is then fed into the DiT for modeling. For image data, we treat it as a video with the shape ***[batch, channels(RGB:3), 1, height, width]***. After encoding, its shape becomes ***[batch, channels(latent:16), 1, height//8, width//8]***.\n### **Q6: Details on Overlapping Fusion Operation.**\nDifferent from MultiDiffusion, which enforces overlapping consistency in the noise space, our proposed overlapping fusion operation is performed in the pixel space, thereby improving spatial continuity. Specifically, we first rotate the four sub-regions (i.e., L, F, R, B) derived from the ViewPoint Map and align them horizontally, as illustrated in Fig. 3. Subsequently, a weighted linear interpolation is performed in pixel space. As shown in Fig. 3, each gradient black diamond shape represents an interpolation weight matrix, where a darker color indicates a smaller weight, and a more transparent color (i.e., closer to the original pixel color of the sub-region) signifies a larger weight. Each subregion is interpolated with its two adjacent faces based on this weight matrix."}]

Author rebuttal:

Meta-review: Following the discussion phases, the majority of reviewers recommended acceptance (1 Accept, 2 Borderline Accepts, 1 Borderline Reject), highlighting the paper’s novelty, clarity of writing, and strong results. The sole negative reviewer expressed an intention to raise their score after the authors addressed experimental concerns, though they did not participate further in the review process. The rebuttal and subsequent exchanges effectively resolved many issues, including the inclusion of results with alternative backbones and evaluations on real videos. Ultimately, all reviewers leaned toward acceptance. Accordingly, the ACs have decided to accept the paper. Please incorporate the reviewers' feedback when preparing the camera-ready version.
Decision: Accept (poster)