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REVIEW 3 major objections 2 minor 54 references

FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation

T0 review · 3 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read FVGen distills a multi-step video diffusion teacher into a four-step student that generates the same number of novel views with equal or better quality and cuts sampling time by more than 90%.

desk verdict The four-step video diffusion distillation idea is worth taking seriously, but the corrupted full text means I can't check the actual experiments—so treat the strong claims as unverified. read the letter →

arxiv 2508.06392 v1 pith:Y77R72JF submitted 2025-08-08 cs.CV

Wenbin Teng , Gonglin Chen , Haiwei Chen , Yajie Zhao This is my paper
classification cs.CV
keywords novelviewsynthesisvideodiffusionmodelsdistillationadversarialtrainingreverseKLdivergencefew-stepsamplingsparse-view3Dreconstruction
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

FVGen is a method for making novel-view synthesis from sparse input images practical by drastically cutting the sampling cost of video diffusion models. It tries to establish that a multi-step video diffusion teacher can be distilled into a student that produces the same number of novel views in only four denoising steps, with visual quality similar to or better than the teacher, while removing more than 90% of the sampling time. If true, this removes the main practical bottleneck that keeps video-diffusion-based sparse-view reconstruction slow, while preserving enough view consistency and diversity for downstream 3D reconstruction. The paper argues that the right distillation recipe pairs an adversarial GAN loss with a softened reverse KL-divergence objective, rather than a standard one-step regression target.

What carries the argument

The central object is a distilled few-step video diffusion student. The mechanism is distribution-matching distillation rather than one-step regression: the student is optimized so that, starting from random noise, its four-step denoising trajectory yields outputs that are judged real by a GAN discriminator trained against the teacher's samples, and that minimize a softened reverse KL divergence with the teacher's output distribution, which punishes the student where the teacher has probability mass and avoids over-smoothing. This combination carries the claim: the GAN term preserves high-frequency detail, and the softened reverse KL term preserves multi-view consistency and diversity across

What would settle it

Run a controlled comparison: for the same sparse-view input, sample many view sets from the four-step student and from the full multi-step teacher; if the student's generated views have measurably lower diversity, or if feeding the student's views into the same reconstruction pipeline yields systematically worse geometry than the teacher's views, the central claim fails. A second check is timing: the claimed reduction of more than 90% should hold end-to-end, not just for the denoising loop.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central discovery is that a video diffusion model used for novel-view synthesis does not need dozens of iterative denoising steps at inference time. FVGen first trains a multi-step video diffusion model as teacher, then distills it into a student that generates the full set of novel views in as few as four sampling steps. The student is trained with a GAN discriminator that matches the perceptual quality and detail of the teacher's output, together with a softened reverse KL-divergence term that keeps the student's distribution close to the teacher's without the mode-averaging that a plain forward KL objective can cause. On real-world datasets, the paper reports

Load-bearing premise

The load-bearing premise is that a four-step student trained with adversarial and softened reverse-KL losses can match the teacher's full output distribution, including view diversity and multi-view consistency, without systematic bias or mode collapse.

Editorial extensions

If this is right

  • If correct, sparse-view 3D reconstruction pipelines can run FVGen instead of a full multi-step video diffusion model and keep similar geometry and visual quality.
  • The same distillation recipe could apply to any pre-trained video diffusion model, not only novel-view synthesis, to obtain few-step generation.
  • The 90% sampling-time reduction makes repeated video diffusion runs practical, which matters for spatial coverage when more than two sparse views need to be connected.
  • Four-step inference brings video-diffusion-based novel view synthesis closer to interactive or real-time use.
  • The student's speed makes it feasible to generate dense observations on demand, filling unseen areas in sparse-view reconstruction without long waits.

Reading between the lines

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

  • Editorially: the GAN-plus-softened-reverse-KL combination may transfer to other conditional video generation tasks, such as inpainting or temporal interpolation, where teacher sampling is the main cost.
  • Editorially: the strongest untested risk is distribution collapse or reduced view diversity; a natural test is to measure the entropy or FID of generated view sets against the teacher, and to check that reconstruction quality degrades gracefully when student views are fed into a 3D reconstruction module.
  • Editorially: if the distillation preserves the teacher's view consistency, FVGen could serve as a fast proposal generator for multi-view stereo or as a data-augmentation engine for training reconstruction networks.
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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 / 2 minor

Summary. The paper introduces FVGen, a framework for accelerating novel-view synthesis from video diffusion models (VDMs). The proposed method distills a multi-step denoising teacher model into a few-step student model using a combination of generative adversarial networks and softened reverse KL-divergence minimization. The central empirical claims are: (1) the student generates the same number of novel views as prior VDM-based methods in as few as four sampling steps; (2) visual quality is similar or better; and (3) sampling time is reduced by more than 90%. The intended downstream benefit is faster sparse-view 3D reconstruction, where VDMs would otherwise need multiple runs.

Significance. If the empirical claims hold, FVGen addresses a real bottleneck in VDM-based novel-view synthesis and sparse-view reconstruction: slow autoregressive or multi-step sampling. A four-step student that preserves quality while reducing runtime by an order of magnitude would be practically significant. The contribution is a distillation methodology applied to video diffusion models, and the paper's framing around downstream reconstruction is a useful application context. However, the significance can be assessed only after the experiments and technical details are verified; the abstract alone provides no quantitative support, and the full text supplied to me is corrupted and unreadable.

major comments (3)
  1. [Full text] The provided manuscript text is corrupted (mojibake) and unreadable. Sections, equations, tables, and experimental results cannot be accessed. As a result, the central empirical claims — four-step sampling, >90% time reduction, and similar/better visual quality — cannot be verified. This is a load-bearing issue for an empirical paper. The authors must provide a readable manuscript with full experimental details.
  2. [Abstract] The abstract reports only qualitative claims ('similar (or even better) visual quality', 'more than 90% time reduction') without any metrics, baseline names, dataset identifiers, or error bars. Since the full text is unavailable, these claims are unsupported. The revision must include concrete evaluation numbers and a clear comparison protocol.
  3. [Objective (GAN + softened reverse KL)] The distillation objective combines a softened reverse KL term and an adversarial GAN loss, both of which are mode-seeking for the student distribution. The paper's downstream goal requires the student to supply diverse observations for sparse-view reconstruction. The abstract reports no teacher-student diversity comparison and no downstream reconstruction robustness metric over multiple student samples. This leaves open the risk that per-frame quality metrics are good while reconstruction-critical variation is lost. Please add an explicit diversity or downstream-consistency evaluation, e.g., measuring variance across student samples or comparing reconstruction metrics against teacher-generated samples.
minor comments (2)
  1. [Abstract] The phrase 'particularly when working with sparse input views (more than 2)' is vague. Clarify the view-count regime and whether 'more than 2' means 3+, or includes 2.
  2. [Abstract] The term 'same number of novel views' is ambiguous: it should state the exact number of generated views and compare it explicitly to prior VDM-based methods.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: FVGen is an empirical distillation method whose quality claims are evaluated against held-out real-world data, not against its own training objective.

full rationale

The paper's derivation chain is an empirical distillation pipeline: a pretrained multi-step video diffusion teacher is used to train a few-step student via GAN and softened reverse-KL losses, and the central claims are (i) the student can match teacher-level novel-view quality and (ii) sampling time is reduced by more than 90%. Neither claim reduces to the training objective by construction. The student is not defined as 'whatever makes the reported metric good'; instead, the evaluation target is external visual quality on real-world datasets, and the speedup is a direct consequence of using four sampling steps versus many. The distillation losses are fitting losses, not re-labeled predictions. There is no self-citation chain invoked to force the method, no uniqueness theorem imported from the authors' prior work, and no ansatz smuggled in via citation that is doing load-bearing work. The review-level concern about mode collapse or insufficient diversity is a plausible robustness/correctness risk, but it is not a circularity: even if the student collapsed to a narrow distribution, the paper's stated quality metrics and sampling-time comparison would remain well-defined external measurements. Since no equation in the provided manuscript equates the prediction with its input, and no fitted parameter is presented as an independent prediction, the honest finding is no significant circularity.

Assumptions & free parameters 3 free parameters · 2 assumptions · 0 invented entities

Because only the abstract is legible, this ledger is necessarily incomplete. I flag the hyperparameters visible in the abstract as free parameters, and the two load-bearing assumptions about teacher quality and distillation sufficiency.

free parameters (3)
  • GAN loss weight
    Abstract says training uses GANs but does not give the relative weighting of adversarial and distillation losses; this is a hand-set hyperparameter.
  • Softened reverse KL temperature or weight
    Abstract mentions softened reverse KL-divergence minimization; the softening coefficient is a free choice not described in the abstract.
  • Number of student sampling steps = 4
    The paper claims 'as few as four' steps; the step count is a design choice not derived from a principle.
assumptions (2)
  • domain assumption A pre-trained multi-step video diffusion model provides a high-quality target distribution for distillation.
    The entire method assumes the teacher VDM is a reliable generator of novel views such that student matching preserves quality.
  • ad hoc to paper Distillation losses (GAN and softened reverse KL) are sufficient to close the distribution gap at four sampling steps.
    This is the core assumption of the method; if the student's distribution collapses or drifts, the quality claim fails.

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Cite this review

Pith. "Pith review of FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation." pith.science (2026). https://pith.science/paper/Y77R72JF

@misc{pith2026250806392,
  author       = {Pith},
  title        = {Pith review of: FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y77R72JF}},
  note         = {Machine review of arXiv:2508.06392}
}
read the original abstract

Recent progress in 3D reconstruction has enabled realistic 3D models from dense image captures, yet challenges persist with sparse views, often leading to artifacts in unseen areas. Recent works leverage Video Diffusion Models (VDMs) to generate dense observations, filling the gaps when only sparse views are available for 3D reconstruction tasks. A significant limitation of these methods is their slow sampling speed when using VDMs. In this paper, we present FVGen, a novel framework that addresses this challenge by enabling fast novel view synthesis using VDMs in as few as four sampling steps. We propose a novel video diffusion model distillation method that distills a multi-step denoising teacher model into a few-step denoising student model using Generative Adversarial Networks (GANs) and softened reverse KL-divergence minimization. Extensive experiments on real-world datasets show that, compared to previous works, our framework generates the same number of novel views with similar (or even better) visual quality while reducing sampling time by more than 90%. FVGen significantly improves time efficiency for downstream reconstruction tasks, particularly when working with sparse input views (more than 2) where pre-trained VDMs need to be run multiple times to achieve better spatial coverage.

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Works this paper leans on

54 extracted references · 23 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

  2. [2]

    Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields

    Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields . Technical report, 2021

    work page 2021

  3. [3]

    Align your latents: High-resolution video synthesis with latent diffusion models

    Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22563--22575, 2023

    work page 2023

  4. [4]

    NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Training

    Dar-Yen Chen, Hmrishav Bandyopadhyay, Kai Zou, and Yi-Zhe Song. Nitrofusion: High-fidelity single-step diffusion through dynamic adversarial training. arXiv preprint arXiv:2412.02030, 2024 a

    work page Pith review arXiv 2024

  5. [5]

    Videocrafter2: Overcoming data limitations for high-quality video diffusion models

    Haoxin Chen, Yong Zhang, Xiaodong Cun, Menghan Xia, Xintao Wang, Chao Weng, and Ying Shan. Videocrafter2: Overcoming data limitations for high-quality video diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7310--7320, 2024 b

    work page 2024

  6. [6]

    Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images

    Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, and Jianfei Cai. Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images. arXiv preprint arXiv:2403.14627, 2024 c

    arXiv 2024

  7. [7]

    Mvsplat360: Feed-forward 360 scene synthesis from sparse views

    Yuedong Chen, Chuanxia Zheng, Haofei Xu, Bohan Zhuang, Andrea Vedaldi, Tat-Jen Cham, and Jianfei Cai. Mvsplat360: Feed-forward 360 scene synthesis from sparse views. 2024 d

    work page 2024

  8. [8]

    V3d: Video diffusion models are effective 3d generators

    Zilong Chen, Yikai Wang, Feng Wang, Zhengyi Wang, and Huaping Liu. V3d: Video diffusion models are effective 3d generators. arXiv preprint arXiv:2403.06738, 2024 e

    arXiv 2024

Show all 54 references
  1. [9]

    Cat3d: Create anything in 3d with multi-view diffusion models

    Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul Srinivasan, Jonathan T Barron, and Ben Poole. Cat3d: Create anything in 3d with multi-view diffusion models. arXiv preprint arXiv:2405.10314, 2024

    2024 arXiv

  2. [10]

    Kl divergence - intuition and examples, n.d

    Dibya Ghosh. Kl divergence - intuition and examples, n.d. Accessed: 2025-03-07

    2025

  3. [11]

    Denoising diffusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33: 0 6840--6851, 2020

    2020

  4. [12]

    Video diffusion models

    Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. arXiv:2204.03458, 2022

    2022 arXiv

  5. [13]

    Cogvideo: Large-scale pretraining for text-to-video generation via transformers

    Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, and Jie Tang. Cogvideo: Large-scale pretraining for text-to-video generation via transformers. arXiv preprint arXiv:2205.15868, 2022

    2022 arXiv

  6. [14]

    Image quality metrics: Psnr vs

    Alain Hore and Djemel Ziou. Image quality metrics: Psnr vs. ssim. In 2010 20th international conference on pattern recognition, pages 2366--2369. IEEE, 2010

    2010

  7. [15]

    Openclip, 2021

    Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt. Openclip, 2021. If you use this software, please cite it as below

    2021

  8. [16]

    Distilling diffusion models into conditional gans

    Minguk Kang, Richard Zhang, Connelly Barnes, Sylvain Paris, Suha Kwak, Jaesik Park, Eli Shechtman, Jun-Yan Zhu, and Taesung Park. Distilling diffusion models into conditional gans. In European Conference on Computer Vision, pages 428--447. Springer, 2024

    2024

  9. [17]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk \"u hler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42 0 (4), 2023

    2023

  10. [18]

    Auto-encoding variational bayes

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

    2013 arXiv

  11. [19]

    Tanks and temples: Benchmarking large-scale scene reconstruction

    Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics (ToG), 36 0 (4): 0 1--13, 2017

    2017

  12. [20]

    Eschernet: A generative model for scalable view synthesis

    Xin Kong, Shikun Liu, Xiaoyang Lyu, Marwan Taher, Xiaojuan Qi, and Andrew J Davison. Eschernet: A generative model for scalable view synthesis. arXiv preprint arXiv:2402.03908, 2024

    2024 arXiv

  13. [21]

    Director3d: Real-world camera trajectory and 3d scene generation from text

    Xinyang Li, Zhangyu Lai, Linning Xu, Yansong Qu, Liujuan Cao, Shengchuan Zhang, Bo Dai, and Rongrong Ji. Director3d: Real-world camera trajectory and 3d scene generation from text. arXiv preprint arXiv:2406.17601, 2024

    2024 arXiv

  14. [22]

    Magic3d: High-resolution text-to-3d content creation

    Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution text-to-3d content creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio...

    2023

  15. [23]

    Sdxl-lightning: Progressive adversarial diffusion distillation

    Shanchuan Lin, Anran Wang, and Xiao Yang. Sdxl-lightning: Progressive adversarial diffusion distillation. arXiv preprint arXiv:2402.13929, 2024

    2024 arXiv

  16. [24]

    Diffusion adversarial post-training for one-step video generation

    Shanchuan Lin, Xin Xia, Yuxi Ren, Ceyuan Yang, Xuefeng Xiao, and Lu Jiang. Diffusion adversarial post-training for one-step video generation. arXiv preprint arXiv:2501.08316, 2025

    2025

  17. [25]

    Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision

    Lu Ling, Yichen Sheng, Zhi Tu, Wentian Zhao, Cheng Xin, Kun Wan, Lantao Yu, Qianyu Guo, Zixun Yu, Yawen Lu, et al. Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pa...

    2024

  18. [26]

    Reconx: Reconstruct any scene from sparse views with video diffusion model

    Fangfu Liu, Wenqiang Sun, Hanyang Wang, Yikai Wang, Haowen Sun, Junliang Ye, Jun Zhang, and Yueqi Duan. Reconx: Reconstruct any scene from sparse views with video diffusion model. arXiv preprint arXiv:2408.16767, 2024

    2024 arXiv

  19. [27]

    Decoupled weight decay regularization

    I Loshchilov. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017

    2017 arXiv

  20. [28]

    You only sample once: Taming one-step text-to-image synthesis by self-cooperative diffusion gans

    Yihong Luo, Xiaolong Chen, Xinghua Qu, Tianyang Hu, and Jing Tang. You only sample once: Taming one-step text-to-image synthesis by self-cooperative diffusion gans. arXiv preprint arXiv:2403.12931, 2024

    2024 arXiv

  21. [29]

    On distillation of guided diffusion models

    Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans. On distillation of guided diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14297--14306, 2023

    2023

  22. [30]

    Srinivasan, Matthew Tancik, Jonathan T

    Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020

    2020

  23. [31]

    Dreamfusion: Text-to-3d using 2d diffusion

    Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988, 2022

    2022 arXiv

  24. [32]

    Accelerating 3d deep learning with pytorch3d

    Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari. Accelerating 3d deep learning with pytorch3d. arXiv:2007.08501, 2020

    2007 arXiv

  25. [33]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj \"o rn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684--10695, 2022

    2022

  26. [34]

    Fast high-resolution image synthesis with latent adversarial diffusion distillation

    Axel Sauer, Frederic Boesel, Tim Dockhorn, Andreas Blattmann, Patrick Esser, and Robin Rombach. Fast high-resolution image synthesis with latent adversarial diffusion distillation. In SIGGRAPH Asia 2024 Conference Papers, pages 1--11, 2024 a

    2024

  27. [35]

    Adversarial diffusion distillation

    Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach. Adversarial diffusion distillation. In European Conference on Computer Vision, pages 87--103. Springer, 2024 b

    2024

  28. [36]

    Mvdream: Multi-view diffusion for 3d generation

    Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308.16512, 2023

    2023 arXiv

  29. [37]

    Denoising diffusion implicit models

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

    2010 arXiv

  30. [38]

    Score-based generative modeling through stochastic differential equations

    Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020 b

    2011 arXiv

  31. [39]

    Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion

    Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts, David Pankratz, Dmitry Tochilkin, Christian Laforte, Robin Rombach, and Varun Jampani. Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion. In European Conference on Computer Vi...

    2025

  32. [40]

    Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation

    Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A Yeh, and Greg Shakhnarovich. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12619--12629, 2023

    2023

  33. [41]

    Dust3r: Geometric 3d vision made easy

    Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vision made easy. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20697--20709, 2024 a

    2024

  34. [42]

    Image quality assessment: from error visibility to structural similarity

    Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13 0 (4): 0 600--612, 2004

    2004

  35. [43]

    Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation

    Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation. Advances in Neural Information Processing Systems, 36, 2024 b

    2024

  36. [44]

    Reconfusion: 3d reconstruction with diffusion priors

    Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P Srinivasan, Dor Verbin, Jonathan T Barron, Ben Poole, et al. Reconfusion: 3d reconstruction with diffusion priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Patt...

    2024

  37. [45]

    Dynamicrafter: Animating open-domain images with video diffusion priors

    Jinbo Xing, Menghan Xia, Yong Zhang, Haoxin Chen, Wangbo Yu, Hanyuan Liu, Gongye Liu, Xintao Wang, Ying Shan, and Tien-Tsin Wong. Dynamicrafter: Animating open-domain images with video diffusion priors. In European Conference on Computer Vision, pages 399--417. Springer, 2025

    2025

  38. [46]

    Depthsplat: Connecting gaussian splatting and depth

    Haofei Xu, Songyou Peng, Fangjinhua Wang, Hermann Blum, Daniel Barath, Andreas Geiger, and Marc Pollefeys. Depthsplat: Connecting gaussian splatting and depth. arXiv preprint arXiv:2410.13862, 2024 a

    2024 arXiv

  39. [47]

    Ufogen: You forward once large scale text-to-image generation via diffusion gans

    Yanwu Xu, Yang Zhao, Zhisheng Xiao, and Tingbo Hou. Ufogen: You forward once large scale text-to-image generation via diffusion gans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8196--8206, 2024 b

    2024

  40. [48]

    One-step diffusion models with f -divergence distribution matching

    Yilun Xu, Weili Nie, and Arash Vahdat. One-step diffusion models with f -divergence distribution matching. arXiv preprint arXiv:2502.15681, 2025

    2025 arXiv

  41. [49]

    Cogvideox: Text-to-video diffusion models with an expert transformer

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

    2024 arXiv

  42. [50]

    One-step diffusion with distribution matching distillation

    Tianwei Yin, Micha \"e l Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Park. One-step diffusion with distribution matching distillation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6613--6623, 2024 a

    2024

  43. [51]

    From slow bidirectional to fast causal video generators

    Tianwei Yin, Qiang Zhang, Richard Zhang, William T Freeman, Fredo Durand, Eli Shechtman, and Xun Huang. From slow bidirectional to fast causal video generators. arXiv preprint arXiv:2412.07772, 2024 b

    2024

  44. [52]

    Improved distribution matching distillation for fast image synthesis

    Tianwei Yin, Micha \"e l Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Fredo Durand, and Bill Freeman. Improved distribution matching distillation for fast image synthesis. Advances in Neural Information Processing Systems, 37: 0 47455--47487, 2025

    2025

  45. [53]

    Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis

    Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien-Tsin Wong, Ying Shan, and Yonghong Tian. Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis. arXiv preprint arXiv:2409.02048, 2024

    2024 arXiv

  46. [54]

    The unreasonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586--595, 2018

    2018

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.