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REVIEW 3 major objections 5 minor 2 cited by

ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A tiled 'ViewPoint map' lets a pretrained perspective video model produce seamless 360-degree video.

desk verdict The ViewPoint map is a genuinely useful new 360-degree representation, but the SOTA claim leans on an in-distribution benchmark and needs a proper held-out evaluation before it can be believed. read the letter →

arxiv 2506.23513 v1 pith:SKSBQA3V submitted 2025-06-30 cs.CV

Zixun Fang , Kai Zhu , Zhiheng Liu , Yu Liu , Wei Zhai , Yang Cao , Zheng-Jun Zha This is my paper
classification cs.CV
keywords panoramicvideogeneration360-degreediffusionmodelsViewPointmapcubemapprojectionequirectangularoutpaintingPano-Perspectiveattention
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that the gap between panoramic and perspective video can be closed by changing the representation rather than retraining from scratch. It introduces the ViewPoint map, a layout of four overlapping pseudo-perspective panels derived from the six faces of a cubemap, and feeds it into a pretrained perspective video diffusion model through a Pano-Perspective attention mechanism. The stated result is high-quality 360-degree video that is spatially consistent across the sphere and temporally dynamic, beating prior panoramic video methods on VBench metrics and in user studies. If true, this would make 360-degree immersive video generation practical by reusing existing perspective video generators and their rich priors.

What carries the argument

The ViewPoint map is the central object: four cube faces are each paired with triangular fragments of adjacent faces to form four square pseudo-perspective panels, arranged so that the up face is split into semicircular regions and reprojected with the scale rule $d(\theta)=a/(\sin\theta+|\cos\theta|)$ to make overlapping seams. Pano-Perspective attention alternates a global attention block over the whole stitched map with per-panel perspective attention blocks, so the model sees both the full sphere and local detail. Overlapping gradient fusion then blends the rhombus-shaped overlaps between subregions with position-dependent weights so adjacent panels merge smoothly. Together these pieces let the model keep the in-context generation behavior of the pretrained diffusion model while learning panoramic consistency.

What would settle it

Reproject a generated ViewPoint map back to equirectangular form and inspect the top face: if the four semicircular patches show visible seams, duplicated content, or inconsistent motion across their overlaps, the claim of seamless polar continuity is falsified.

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Extended reading notes

Core claim

The central claim is that panoramic video can be generated from a perspective input clip and a text prompt by reprojecting the sphere into a ViewPoint map and fine-tuning a pretrained perspective video diffusion model (Wan2.1) on it. The paper reports that this representation removes the polar distortion of equirectangular projections and the hard seams of cubemaps, that the Pano-Perspective attention keeps opposite directions aligned while preserving local detail and motion, and that the resulting videos are more spatially and temporally consistent than those of 360DVD, Imagine360, and Follow-Your-Canvas. The paper therefore presents ViewPoint as a state-of-the-art framework for panoramic video generation.

Load-bearing premise

The method assumes that after the triangular reprojection and overlapping fusion, the ViewPoint map still looks enough like ordinary perspective video that the pretrained model keeps its perspective priors instead of having to relearn basic image statistics.

Editorial extensions

If this is right

  • Panoramic video generation inherits the motion and quality priors of a state-of-the-art perspective video generator, so high-quality 360-degree results do not require a large corpus of panoramic video.
  • Because the method accepts both an input video and a text prompt, it supports conditional video-to-panorama outpainting rather than only text-to-panorama generation.
  • The reported VBench numbers indicate that spatial consistency and high dynamics can be obtained together, a combination earlier panoramic video methods did not achieve.
  • The ViewPoint representation itself, rather than the diffusion backbone, is the main bridge between perspective-pretrained models and spherical video output.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same ViewPoint layout could be applied to single-image panorama generation with an image diffusion model, which would test whether the representation alone transfers across model families.
  • A stress test the paper does not run is to generate long clips and measure drift across the top and bottom faces over time, since polar reprojection is the most likely place for the learned prior to slip.
  • The 4x batch reshape used for perspective attention implies each panel is processed semi-independently; ablating panel size or the number of panels could reveal how much global context is actually required.
  • If the representation transfers cleanly, it may also benefit neighboring tasks such as 360-degree inpainting, spherical super-resolution, and world-model training by giving diffusion models a perspective-like canvas for spherical data.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper introduces ViewPoint, a framework for generating 360-degree panoramic videos from a perspective video clip and text prompt. The core idea is a novel panorama representation, the ViewPoint map, which rearranges six cube faces into four overlapping pseudo-perspective subregions with gradient-based fusion, aiming to combine the local fidelity of perspective projections with global spatial continuity. The method fine-tunes a pretrained perspective video diffusion model (Wan2.1) using a Pano-Perspective attention mechanism that alternates global self-attention over the whole map and local attention over individual subregions. Experiments compare against 360DVD, Imagine360, and Follow-Your-Canvas using VBench metrics on the ODV360 dataset, together with qualitative examples and a user study, and the paper claims state-of-the-art performance.

Significance. If the results are validated, the ViewPoint map is a meaningful contribution: it addresses a known limitation of equirectangular and cubemap representations for generative video models, and the overlapping-fusion idea is a plausible mechanism for preserving cross-face consistency while exploiting perspective priors. The paper includes a useful ablation over ERP, cubemap variants, and the attention design, and the qualitative results show clear improvements in polar-region distortion and temporal consistency over previous approaches. However, the quantitative evidence for the central SOTA claim is currently undermined by an evaluation setup that does not separate training and test data, so the significance of the empirical contribution is not yet established.

major comments (3)
  1. [4.1 and 4.3] The quantitative evaluation in Table 1 is not a valid test of generalization because ODV360 is listed in Sec. 4.1 as one of the four fine-tuning datasets. The paper never specifies a train/test partition, sample IDs, or any held-out subset. Without that information, the margins in Table 1 (e.g., +0.0258 subject consistency over 360DVD, +0.0908 dynamic degree over Imagine360) could reflect in-distribution memorization rather than a generalizable advantage. Please report exactly which ODV360 videos were used for training, which for evaluation, or re-evaluate on a dataset not seen during training, and report error bars or significance tests.
  2. [3.4, Eqs. (9)-(12)] The overlapping fusion equations contain notation inconsistencies that prevent verification of the algorithm. In Eq. (9), the first branch uses `R−90(W)` while the second branch uses `Rot90(W)`; elsewhere `R90` and `R−90` are defined in Eqs. (6)-(7), and `Rot90` is never defined. Similarly, Eq. (10) first branch applies `R−90(W)` while the second applies `R90(W)` to the overlapping region of the same subregion, which is suspicious. Please unify the notation, define `Rot90`, and provide a derivation or pseudocode that makes the overlap regions and weight indexing unambiguous.
  3. [4.5] The user study is underspecified and cannot substitute for the missing held-out quantitative evaluation. The paper states only that 50 valid questionnaires were collected with 14 video sets each, with no details on participant recruitment, instructions, interface, randomization, or inter-rater agreement. Figure 7 shows raw vote counts without error bars or any statistical test, so the claim that our method 'receives the highest number of votes across all four dimensions' has no demonstrated reliability.
minor comments (5)
  1. [3.1, Eq. (1)] The notation in the preliminary is confusing: the sentence says 'a noisy latent xt is obtained by adding a random noise x0 ∼ N(0, I)' but x0 is already used for the clean latent. Please rename the noise variable (e.g., x1) consistently with Eq. (1).
  2. [3.2] The phrase 'located to the left, right, above, and below L, respectively' appears to contain a typo: for a central face F, the adjacent faces should be to the left, right, above, and below F, not L. Please correct.
  3. [4.3] The text says the evaluation covers 'five dimensions' but then lists only four: subject consistency, imaging quality, motion smoothness, and dynamic degree. Either add the fifth dimension or correct the count.
  4. [3.3] The feature shape is written as `(batch_size, channels, f rames, height, width)` with a space in 'frames', and the reshape to `height/2, width/2` is not explained. Clarify why the two spatial dimensions are halved after Pano attention.
  5. [2] The related-work discussion of panoramic video generation omits 4K4DGEN from the quantitative comparison though it is cited; if it is not comparable due to task definition, a brief sentence explaining its exclusion would help.

Circularity Check

1 steps flagged · score 6.0 of 10

The SOTA claim is supported only by VBench scores computed on ODV360, which is also a fine-tuning dataset; no held-out split is reported, so the headline result is an in-distribution evaluation rather than an independent prediction.

  1. fitted input called prediction [Section 4.1 (Datasets & Preprocess) and Section 4.3 (Quantitative Comparison), Table 1]
    "Our model is trained on 4 panorama datasets, including one image dataset, Flickr360, and three video datasets, WEB360, ODV360, and 360+x. ... We evaluate our approach and previous methods on the ODV360 dataset across five dimensions."

    The model's weights are optimized on ODV360 during fine-tuning (Sec. 4.1), and then the VBench scores that support the 'state-of-the-art performance' claim are computed on ODV360 (Sec. 4.3, Table 1). No train/test split, sample IDs, or held-out subset is reported. The reported margins (subject consistency 0.8793 vs 0.8633; dynamic degree 0.9083 vs 0.8148) are therefore training-set scores, not held-out predictions. Since the comparison methods are not reported as being fine-tuned on ODV360, the comparison is also uneven: the proposed method is fit to the test distribution, while the baselines are not. The 'prediction' of superior VBench performance reduces to an in-distribution fit.

full rationale

The paper's representation design (ViewPoint map, Pano-Perspective attention, overlapping fusion) is not circular: it is a geometric construction applied to cubemap/ERP data, and its components are not fitted to the evaluation metric. The use of the pretrained Wan2.1 model is also not circular, as it is an external, publicly available model rather than a self-citation chain. However, the central empirical claim of 'state-of-the-art performance and surpassing previous methods' rests entirely on Table 1, which reports VBench scores on ODV360. Section 4.1 explicitly lists ODV360 as one of the four datasets used to fine-tune the model, and Section 4.3 evaluates the method on ODV360 without disclosing any held-out partition. This makes the quantitative 'prediction' an in-distribution training-set measurement. The absence of a reported split or sample IDs means the reported SOTA margins cannot be interpreted as evidence of generalization. This is a partial circularity affecting the headline claim, but it does not invalidate the non-empirical contributions of the representation and attention design. Score 6 reflects that one of the paper's key 'predictions' reduces to a fit on the evaluation set.

Assumptions & free parameters 0 free parameters · 3 assumptions · 1 invented entities

The method relies on the transferability of pretrained perspective priors and on the hand-designed geometric construction of the ViewPoint map. The geometric construction is an ad hoc design choice with no independent validation, and the evaluation's circularity (training on the evaluation dataset) is a separate concern.

assumptions (3)
  • domain assumption Pretrained perspective video diffusion models contain priors that can be transferred to panoramic content through fine-tuning on a small amount of 360-degree data.
    This is the core premise enabling the method; if false, the fine-tuned model cannot generate high-quality panoramas.
  • ad hoc to paper The geometric construction of the ViewPoint map (Eqs. 3-4) preserves spatial continuity and produces inputs that lie close to the perspective image distribution.
    The U-face projection and the rotation/concat scheme are hand-designed and not derived from first principles or validated independently.
  • standard math Standard properties of perspective projection and image rotations are correct as used.
    Elementary geometry used in the mapping.
invented entities (1)
  • ViewPoint map
    purpose: A panorama representation that combines global continuity with perspective-like local appearance for diffusion model input.
    The representation is new in this paper; there is no external evidence of its validity beyond the paper's own experiments.

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Cite this review

Pith. "Pith review of ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models." pith.science (2026). https://pith.science/paper/SKSBQA3V

@misc{pith2026250623513,
  author       = {Pith},
  title        = {Pith review of: ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SKSBQA3V}},
  note         = {Machine review of arXiv:2506.23513}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2506.23513 by the authors.

Figure 1
Figure 1. The generated result. The image at the bottom displays the ViewPoint map generated by our model, with the background image showing the concatenated equirectangular map derived from the ViewPoint map. The generated panoramic image exhibits excellent spatial consistency, as the equirectangular map can be seamlessly stitched together. Project page: ViewPoint. Abstract Panoramic video generation aims to synthesize 360-d… view at source ↗
Figure 2
Figure 2. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Overlapping fusion. The four subregions partially overlap with each other, thus we propose a gradient fusion mechanism to interpolate the overlapping areas, thereby enhancing spatial consistency. For each subregion Sd ∈ R r×r , r = 2n, d ∈ [L, F, R, B], the overlapping fusion process can be described by the following formulas: Wi,j = i + j − 2 2(n − 1) , i, j = 1, 2, . . . , n (5) where i, j denote the row and colum… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Results from 360DVD [31] and Imagine360 [25]. 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, severe…
Figure 5
Figure 5. Figure 5: Qualitative comparison of generated videos. 360DVD is a text-driven approach and exhibits terrible image quality while Follow-Your-Canvas fails to generate panoramic videos with a reasonable spatial layout. Imagine360 suffers from spatial-temporal discontinuity. Our ap…
Figure 6
Figure 6. Figure 6: Ablation on 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 gener…
Figure 7
Figure 7. 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. In this work, we present ViewPoint, a novel framework for representing and generating panoramic videos leveraging moder…

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Forward citations

Cited by 2 Pith papers

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  1. SHERPA: Seam-aware Harmonized ERP Adaptation for Open-Domain 360$^\circ$ Panorama Generation

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  2. Rein3D: Reinforced 3D Indoor Scene Generation with Panoramic Video Diffusion Models

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    Rein3D generates photorealistic, globally consistent 3D indoor scenes by using a restore-and-refine process where radial panoramic videos are restored via diffusion models and then used to update a 3D Gaussian field.

Reference graph

Works this paper leans on

34 extracted references · 26 canonical work pages · cited by 2 Pith papers

  1. [1]

    A survey of representation learning, optimization strategies, and applications for omnidirectional vision

    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

    work page 2025

  2. [2]

    Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond

    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

    arXiv 2023

  3. [3]

    Ntire 2023 challenge on 360deg omnidirectional image and video super-resolution: Datasets, methods and results

    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

    work page 2023

  4. [4]

    360+x: A panoptic multi-modal scene understanding dataset

    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

    work page 2024

  5. [5]

    Follow-your-canvas: Higher-resolution video outpainting with extensive content generation

    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

    arXiv 2024

  6. [6]

    Text2light: Zero-shot text-driven hdr panorama generation

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

    work page 2022

  7. [7]

    Scaling rectified flow transformers for high-resolution image synthesis

    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

    work page 2024

  8. [8]

    Diffusion360: Seamless 360 degree panoramic image generation based on diffusion models

    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

    arXiv 2023

Show all 34 references
  1. [9]

    Animatediff: Animate your personalized text-to-image diffusion models without specific tuning

    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

    2024

  2. [10]

    Denoising diffusion probabilistic models

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

    2020

  3. [11]

    In-context lora for diffusion transformers

    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

    2024 arXiv

  4. [12]

    VBench: Comprehensive benchmark suite for video generative models

    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. I...

    2024

  5. [13]

    Cubediff: Repurposing diffusion-based image models for panorama generation

    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

    2025

  6. [14]

    Auto-encoding variational bayes

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

    2013 arXiv

  7. [15]

    Panogen: Text-conditioned panoramic environment generation for vision-and- language navigation

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

    2023

  8. [16]

    4k4dgen: Panoramic 4d generation at 4k resolution

    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

    2024

  9. [17]

    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

    2023

  10. [18]

    Panofree: Tuning-free holistic multi-view image generation with cross-view self-guidance

    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

    2024

  11. [19]

    Vidpanos: Generative panoramic videos from casual panning videos

    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. 10

    2024

  12. [20]

    Scalable diffusion models with transformers

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

    2023

  13. [21]

    High-resolution image synthesis with latent diffusion models

    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 10684– 10695, 2022

    2022

  14. [22]

    Conditional 360-degree image synthesis for immersive indoor scene decoration

    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

    2023

  15. [23]

    Denoising diffusion implicit models

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

    2010 arXiv

  16. [24]

    Denoising diffusion implicit models

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

    2021

  17. [25]

    Imagine360: Immersive 360 video generation from perspective anchor

    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

    2024 arXiv

  18. [26]

    Mvdiffusion: Enabling holistic multi-view image generation with correspondence-aware diffusion

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

    2023

  19. [27]

    Wan: Open and advanced large-scale video generative models

    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, Pin...

    2025 arXiv

  20. [28]

    Be-your-outpainter: Mastering video outpainting through input-specific adaptation

    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

    2024

  21. [29]

    Customizing 360-degree panoramas through text-to-image diffusion models

    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

    2024

  22. [30]

    360-degree panorama generation from few unregistered nfov images

    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

    2023

  23. [31]

    360dvd: Controllable panorama video generation with 360-degree video diffusion model

    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

    2024

  24. [32]

    Panodiffusion: 360-degree panorama outpainting via diffusion

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

    2023

  25. [33]

    Diffpano: Scalable and consistent text to panorama generation with spherical epipolar-aware diffusion

    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

    2024

  26. [34]

    Taming stable diffusion for text to 360◦ panorama image generation

    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. 11

    2024

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