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REVIEW 3 major objections 4 minor 1 cited by

NVComposer: Boosting Generative Novel View Synthesis with Multiple Sparse and Unposed Images

T0 review · 3 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read NVComposer claims pose-free generative novel view synthesis works from multiple unposed images, with quality rising as more views are added, by having the diffusion model infer camera geometry implicitly during generation.

desk verdict The dual-stream pose-free NVS design is genuinely novel and the RealEstate10K evidence holds up, but Table 2's DL3DV rows appear copied from Table 1's medium RealEstate10K rows, which undermines the SOTA claim until the authors explain it. read the letter →

arxiv 2412.03517 v2 pith:XGQYTXXR submitted 2024-12-04 cs.CV

Lingen Li , Zhaoyang Zhang , Yaowei Li , Jiale Xu , Wenbo Hu , Xiaoyu Li , Weihao Cheng , Jinwei Gu
show 2 more authors
Tianfan Xue Ying Shan
This is my paper · ORCID
classification cs.CV
keywords novelviewsynthesisgenerativediffusionpose-freemulti-viewcameraposeestimationgeometrydistillationvideopriorsparseviews
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

NVComposer claims that generative novel view synthesis can work from multiple sparse images with no camera poses and no pre-reconstruction, by letting the diffusion model itself infer the spatial relationships among the condition views. The paper introduces an image-pose dual-stream diffusion model that jointly denoises target images and condition-view pose embeddings, and a geometry-aware feature alignment module that distills geometric priors from DUSt3R during training. The authors report that as the number of unposed input views grows from one to four, NVComposer's PSNR rises consistently on RealEstate10K and DL3DV, whereas the external-alignment baseline ViewCrafter degrades in the hard, low-overlap regime. If correct, this removes the main practical bottleneck of multi-view generative NVS and makes it accessible for images captured without pose estimation.

What carries the argument

The load-bearing design is the image-pose dual-stream diffusion model: target and condition frames are represented as image-pose bundles, sequences of $[\text{VAE latent}, \text{Plücker ray embedding}]$ tensors, and the model is trained so that the condition segment is masked (zeroed images in the target segment, zeroed poses in the condition segment) and must be jointly denoised. A separate pose decoding head, with one-tenth the channels and no attention, denoises the pose channels to avoid interference between high-frequency image latents and low-frequency pose embeddings. The geometry-aware feature alignment adapter then maps internal U-Net features, after each spatio-temporal self-attention layer, through channel-wise MLPs and a residual block to regress DUSt3R's concatenated pointmaps via MSE; this distills geometry during training but is dropped at inference.

What would settle it

Fix a test set of wide-baseline view pairs where DUSt3R's own pointmaps are known to be poor (measured, say, by the reported PSNR drop). Train NVComposer with the alignment loss on those pairs replaced by random or zeroed pointmaps, and compare NVS quality to the full model. If removing the noisy alignment signal does not hurt (or helps), the geometry distillation is not doing the claimed load-bearing work. Alternatively, on the hard RealEstate10K subset, verify that NVComposer's pose predictions remain accurate when the condition views have zero visible overlap; if the dual-stream hallucinates plausible but wrong poses, the 'implicit inference of spatial relationships' claim fails.

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Extended reading notes

Core claim

The central discovery is that explicit multi-view alignment—pose estimation or pre-reconstruction—is not necessary for generative novel view synthesis; a diffusion model can implicitly infer the relative geometry of the conditioning views while generating the target views. NVComposer achieves this by training on image-pose bundles, where each bundle concatenates the latent image with a Plücker ray embedding of its camera pose, and masking the pose channels of the condition segment so that the model must fill them in. The geometry-aware feature alignment loss pushes the model's internal features to match pointmaps produced by DUSt3R on the same training views, transferring 3D knowledge without requiring DUSt3R at inference. The paper demonstrates state-of-the-art PSNR, SSIM, LPIPS, and DISTS against MotionCtrl, CameraCtrl, DUSt3R, and ViewCrafter, and shows that quality improves monotonically when more unposed views are added, including on hard test sets with 60–120 degree separations between condition views.

Load-bearing premise

The geometry alignment loss in Eq. (1) assumes that DUSt3R's pointmaps are accurate enough across the training distribution—especially for sparse and low-overlap views—to teach correct geometry; if those pointmaps are noisy in that regime, the alignment term injects misleading supervision, and the claimed robustness to unposed inputs is not guaranteed.

Editorial extensions

If this is right

  • With more unposed input views, NVComposer's PSNR rises from 18.72 to 22.55 on RealEstate10K easy, from 15.21 to 17.82 on hard, and from 15.31 to 17.92 on DL3DV, showing the model exploits additional views without pose labels.
  • The implicit pose estimates from the dual-stream decoder are more accurate than DUSt3R's explicit correspondences on sparse low-overlap inputs (rotation error 5.86 vs 58.40 degrees on the hard subset).
  • The method transfers to synthetic objects: NVComposer beats SV3D with one view and improves further with 2 and 4 unposed views.
  • Since no reconstruction step runs at inference, NVComposer is end-to-end and avoids the failure mode of external alignment collapses in sparse low-overlap settings.
  • The ablation tables indicate both components matter: removing dual-stream drops PSNR from 17.05 to 14.69 on Objaverse, and removing alignment drops PSNR from 15.66 to 14.72 on RealEstate10K.

Reading between the lines

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

  • If the monotonic scaling with unposed views holds out-of-distribution, NVComposer's recipe could slot into 3D content pipelines (e.g., scene editing, texture transfer, or dataset augmentation) where SfM is unavailable or fails.
  • The dual-stream's pose predictions are stochastic: sampling multiple pose hypotheses for ambiguous inputs could quantify or reduce uncertainty in downstream geometry, something the paper does not explore.
  • A harder test than the reported benchmarks is fully unposed photo collections from the wild (different cameras, lighting, and partial occlusion); the current training on video frames and Objaverse renderings may not transfer to that setting.
  • The geometry alignment loss effectively treats DUSt3R as a fixed teacher; one could test whether distilling from a stronger teacher (e.g., a wide-baseline stereo model) widens the gap over ViewCrafter in the low-overlap regime.
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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 / 4 minor

Summary. The paper introduces NVComposer, a generative novel view synthesis model that takes one to four unposed images as input and produces target novel views along with estimated camera poses for the condition views. The method combines an image-pose dual-stream diffusion model, initialized from DynamiCrafter, with a geometry-aware feature alignment loss that distills DUSt3R pointmaps into the diffusion features during training; no external pose estimation or pre-reconstruction is needed at inference. The central empirical claim is that NVComposer achieves state-of-the-art generative NVS quality and that quality improves monotonically as the number of unposed input views increases, supported by experiments on RealEstate10K, DL3DV, and Objaverse.

Significance. If the empirical claims hold, the paper makes a useful contribution: it removes the reliance on explicit multi-view alignment in generative NVS and shows a plausible mechanism, dual-stream pose generation plus geometry distillation, for using extra unposed views. The architecture is described in enough detail to be credible, and the paper includes ablations isolating both main components as well as distribution-level metrics (FID, FVD, KVD). However, the evidence base has a serious table inconsistency and small evaluation sets without variance or significance estimates, so the state-of-the-art claim is not yet established at the reported strength.

major comments (3)
  1. [Section 4.2.1, Tables 1 and 2] The DL3DV results for NVComposer with one and two views are numerically identical to the RealEstate10K medium results for NVComposer with one and two views. Table 2 lists PSNR/SSIM/LPIPS/DISTS = 15.3101/0.6056/0.3445/0.1516 for one view and 16.9100/0.6445/0.2742/0.1198 for two views, which exactly match the 'Medium' columns of Table 1. Four-decimal identity across two different datasets is not a plausible coincidence. Since DL3DV is one of only two scene benchmarks supporting the state-of-the-art and the 'more views improve quality' claim, this duplication is load-bearing. The authors should re-run and report the DL3DV evaluation or correct the tables; as written, the DL3DV evidence for the main claims is unsupported.
  2. [Section 3.2, Eq. (1); Section 4.2.1, Table 1 and Table 7] The geometry-aware alignment loss supervises internal diffusion features with DUSt3R pointmaps D(I1, It). The paper motivates pose-free NVS by the failure of external alignment on hard sparse views, but Table 1 shows DUSt3R's own NVS quality in the hard set is poor (e.g., PSNR 11.5381 with two views), and Table 7 reports DUSt3R pose error ΔR = 58.40 degrees on the hard subset. This indicates the teacher is unreliable in exactly the regime the method targets. The paper should provide evidence that noisy pointmap supervision in this regime does not hurt the claimed robustness, for example by ablating the alignment loss weight, reporting the alignment loss magnitude across easy/medium/hard subsets, or comparing with a stronger teacher.
  3. [Section 4.2.1, Benchmark Settings] The scene-level evaluation uses 20, 60, and 20 samples for RealEstate10K and 20 scenes for DL3DV, with no variance estimates or significance tests. Many reported differences between methods are small; for example, in Table 3 the FID of NVComposer (46.19) is slightly worse than ViewCrafter (46.08), and the text calls these comparable without a statistical test. The sample selection is described only as 'randomly select' without a seed or fixed split. Please report confidence intervals or per-sample distributions and specify the exact evaluation split so that the state-of-the-art claim can be properly assessed.
minor comments (4)
  1. [Section 1, Introduction] The phrase 'spare unposed images' should be 'sparse unposed images' for consistency with the rest of the paper.
  2. [Section 4.2.1, Results] The sentence 'It it noteworthy that...' contains a typo: 'It it' should be 'It is'.
  3. [Section 3.3, Eq. (2)] Equation (2) uses the expectation subscript Scond in the definition but the notation Bc elsewhere; please unify the notation for the conditional input and clarify whether the expectation is over the same random variables as in the training procedure.
  4. [Section 4.2.2, Table 4] The object-level evaluation reports only PSNR, SSIM, and LPIPS, while the scene-level evaluation also reports DISTS; please state whether the DISTS metric was not evaluated on Objaverse or was omitted for space, and clarify the number of test objects used for the SV3D comparison.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: dual-stream pose conditioning and DUSt3R feature alignment are external supervisions, and no equation reduces a prediction to a fitted input. The DL3DV table duplication is a non-circular data-integrity concern.

full rationale

The claimed derivation chain is not circular. The image-pose dual-stream model is trained with ground-truth poses and target images from RealEstate10K, DL3DV, CO3D, and Objaverse, and the geometry-aware adapter in Eq. (1) regresses internal diffusion features to pointmaps D(I1, It) produced by the external DUSt3R model. Neither the pose stream nor the alignment loss uses NVComposer's own generated views or predicted poses as supervision, so the SOTA and scaling claims are empirical evaluations rather than reductions of input to output. Self-citations to ViewCrafter, MotionCtrl, and DynamiCrafter appear as baselines or as backbone initialization, but the central argument does not rest on any uniqueness theorem or unverified claim from those papers; the model is evaluated against held-out reference views and against independent external methods. The DL3DV rows in Table 2 showing the same four metrics to four decimals as the RealEstate10K medium rows in Table 1 are a serious reporting inconsistency that should be corrected and would weaken the DL3DV evidence, but duplication of numbers is not a circular derivation; it is an integrity issue. No step in the paper's equations equates a fitted parameter with a predicted quantity, so no circularity score above 0 is warranted.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the reliability of pretrained external models (DynamiCrafter as a generative prior, DUSt3R as a geometric teacher), on the anchor-view coordinate convention, and on the representativeness of the mixed training data. No fitted values are reported for the loss weight lambda. No new physical entities are introduced.

free parameters (2)
  • lambda (alignment loss weight) = not reported
    Eq. (3) combines diffusion loss and alignment loss with weight lambda; the value is never stated, so the balance between the two objectives is a free choice.
  • pose decoding head channel factor = 0.1
    Sec. 3.1 sets the base channel number of the pose decoding head to one-tenth of the image decoder; chosen empirically, not derived.
assumptions (5)
  • domain assumption Pretrained DynamiCrafter video diffusion weights provide a useful generative prior for multi-view synthesis.
    Sec. 3.1 initializes the dual-stream model from DynamiCrafter; if this prior does not transfer to NVS, the whole architecture underperforms.
  • domain assumption DUSt3R pointmaps are accurate enough to serve as geometric supervision targets in Eq. (1).
    Sec. 3.2 uses D(I1, It) as ground-truth geometry; the paper itself shows DUSt3R degrades on sparse hard views.
  • ad hoc to paper The anchor-view duplication fixes a valid relative coordinate system.
    Sec. 3.1 designates the first element of the target segment as a copy of the condition anchor with identity extrinsics; this is a design choice specific to this method, and its failure would corrupt all predicted poses.
  • domain assumption Plucker ray embeddings generated by the model can be converted to camera poses by the solver of Cameras as Rays [45].
    Sec. 4.3 uses method [45] to decode generated ray fields; any approximation error transfers to the pose accuracy claim.
  • domain assumption The mixed training set (Objaverse, RealEstate10K, CO3D, DL3DV) covers the test distributions.
    Sec. 4.1 trains on these datasets; no domain-shift analysis is provided for the DL3DV and Objaverse test sets.

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Cite this review

Pith. "Pith review of NVComposer: Boosting Generative Novel View Synthesis with Multiple Sparse and Unposed Images." pith.science (2026). https://pith.science/paper/XGQYTXXR

@misc{pith2026241203517,
  author       = {Pith},
  title        = {Pith review of: NVComposer: Boosting Generative Novel View Synthesis with Multiple Sparse and Unposed Images},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XGQYTXXR}},
  note         = {Machine review of arXiv:2412.03517}
}
read the original abstract

Recent advancements in generative models have significantly improved novel view synthesis (NVS) from multi-view data. However, existing methods depend on external multi-view alignment processes, such as explicit pose estimation or pre-reconstruction, which limits their flexibility and accessibility, especially when alignment is unstable due to insufficient overlap or occlusions between views. In this paper, we propose NVComposer, a novel approach that eliminates the need for explicit external alignment. NVComposer enables the generative model to implicitly infer spatial and geometric relationships between multiple conditional views by introducing two key components: 1) an image-pose dual-stream diffusion model that simultaneously generates target novel views and condition camera poses, and 2) a geometry-aware feature alignment module that distills geometric priors from dense stereo models during training. Extensive experiments demonstrate that NVComposer achieves state-of-the-art performance in generative multi-view NVS tasks, removing the reliance on external alignment and thus improving model accessibility. Our approach shows substantial improvements in synthesis quality as the number of unposed input views increases, highlighting its potential for more flexible and accessible generative NVS systems. Our project page is available at https://lg-li.github.io/project/nvcomposer

Figures

Figures reproduced from arXiv: 2412.03517 by the authors.

Figure 1
Figure 1. As the number of unposed input views increases, NVComposer (blue circle) effectively uses the extra information to improve [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Framework illustration of NVComposer. It contains an [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Structure of the geometry-aware feature alignment [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Visual comparison of NVS results on the RealEstate10K [ [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Visual comparison of novel view generation results on [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: A visual sample in the ablation results of the geometry [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

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Works this paper leans on

47 extracted references · 16 canonical work pages · cited by 1 Pith paper

  1. [1]

    Stable video diffusion: Scaling latent video diffusion models to large datasets

    Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram V oleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127, 2023. 3

    arXiv 2023

  2. [2]

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

    Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dock- horn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with la- tent diffusion models. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 22563–22575, 2023. 3

    2023

  3. [3]

    Generative novel view synthesis with 3d-aware diffusion models

    Eric R Chan, Koki Nagano, Matthew A Chan, Alexander W Bergman, Jeong Joon Park, Axel Levy, Miika Aittala, Shalini De Mello, Tero Karras, and Gordon Wetzstein. Generative novel view synthesis with 3d-aware diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4217–4229, 2023. 2

    2023

  4. [4]

    Objaverse: A universe of annotated 3d objects

    Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objaverse: A universe of annotated 3d objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13142–13153, 2023. 2, 5, 7, 8

    work page 2023

  5. [5]

    Image quality assessment: Unifying structure and texture similarity

    Keyan Ding, Kede Ma, Shiqi Wang, and Eero P Simoncelli. Image quality assessment: Unifying structure and texture similarity. IEEE transactions on pattern analysis and ma- chine intelligence, 44(5):2567–2581, 2020. 5

    2020

  6. [6]

    Structure and content-guided video synthesis with diffusion models

    Patrick Esser, Johnathan Chiu, Parmida Atighehchian, Jonathan Granskog, and Anastasis Germanidis. Structure and content-guided video synthesis with diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7346–7356, 2023. 3

    2023

  7. [7]

    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. 1, 3, 4

    arXiv 2024

  8. [8]

    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. arXiv preprint arXiv:2307.04725, 2023. 3

    arXiv 2023

Show all 47 references
  1. [9]

    Cameractrl: Enabling camera control for text-to-video generation

    Hao He, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. Cameractrl: Enabling camera control for text-to-video generation. arXiv preprint arXiv:2404.02101, 2024. 3, 5, 6, 7

    2024 arXiv

  2. [10]

    Gans trained by a two time-scale update rule converge to a local nash equilib- rium

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib- rium. Advances in neural information processing systems , 30, 2017. 6

    2017

  3. [11]

    Denoising dif- fusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020. 2

    2020

  4. [12]

    Video dif- fusion models

    Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video dif- fusion models. Advances in Neural Information Processing Systems, 35:8633–8646, 2022. 3

    2022

  5. [13]

    Lora: Low-rank adaptation of large language models

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. 3

    2021 arXiv

  6. [14]

    Spad: Spatially aware multi-view diffusers

    Yash Kant, Aliaksandr Siarohin, Ziyi Wu, Michael Vasilkovsky, Guocheng Qian, Jian Ren, Riza Alp Guler, Bernard Ghanem, Sergey Tulyakov, and Igor Gilitschenski. Spad: Spatially aware multi-view diffusers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- ter...

    2024

  7. [15]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139–1,

  8. [16]

    Ground- ing image matching in 3d with mast3r

    Vincent Leroy, Yohann Cabon, and J´erˆome Revaud. Ground- ing image matching in 3d with mast3r. arXiv preprint arXiv:2406.09756, 2024. 1

    2024 arXiv

  9. [17]

    Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

    Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In In- ternational conference on machine learning , pages 19730– 19742. PMLR, 2023. 4

    2023

  10. [18]

    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 , p...

    2024

  11. [19]

    Re- conx: 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. Re- conx: Reconstruct any scene from sparse views with video diffusion model. arXiv preprint arXiv:2408.16767, 2024. 1, 2, 3, 4

    2024 arXiv

  12. [20]

    Zero-1-to- 3: Zero-shot one image to 3d object

    Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tok- makov, Sergey Zakharov, and Carl V ondrick. Zero-1-to- 3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9298–9309, 2023. 1, 3

    2023

  13. [21]

    Wonder3d: Sin- gle image to 3d using cross-domain diffusion

    Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. Wonder3d: Sin- gle image to 3d using cross-domain diffusion. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pa...

    2024

  14. [22]

    Nerf: Representing scenes as neural radiance fields for view syn- thesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. Communications of the ACM , 65(1):99–106, 2021. 1

    2021

  15. [23]

    Learning transferable visual models from natural language supervi- sion

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi- sion. In International conference on machine learning, ...

    2021

  16. [24]

    Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction

    Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Com- mon objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. In Proceedings of the IEEE/CVF international conference on computer vi...

    2021

  17. [25]

    Pixel- synth: Generating a 3d-consistent experience from a single image

    Chris Rockwell, David F Fouhey, and Justin Johnson. Pixel- synth: Generating a 3d-consistent experience from a single image. In Proceedings of the IEEE/CVF International Con- ference on Computer Vision, pages 14104–14113, 2021. 1, 2

    2021

  18. [26]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn 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. 3

    2022

  19. [27]

    Zero123++: a single image to consistent multi-view dif- fusion base model

    Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, and Hao Su. Zero123++: a single image to consistent multi-view dif- fusion base model. arXiv preprint arXiv:2310.15110, 2023. 1

    arXiv

  20. [28]

    Light field networks: Neu- ral scene representations with single-evaluation rendering

    Vincent Sitzmann, Semon Rezchikov, Bill Freeman, Josh Tenenbaum, and Fredo Durand. Light field networks: Neu- ral scene representations with single-evaluation rendering. Advances in Neural Information Processing Systems , 34: 19313–19325, 2021. 3

    2021

  21. [29]

    Denoising diffusion implicit models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 2

    2010 arXiv

  22. [30]

    Single-view view synthe- sis with multiplane images

    Richard Tucker and Noah Snavely. Single-view view synthe- sis with multiplane images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 551–560, 2020. 2

    2020

  23. [31]

    To- wards accurate generative models of video: A new metric & challenges

    Thomas Unterthiner, Sjoerd Van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly. To- wards accurate generative models of video: A new metric & challenges. arXiv preprint arXiv:1812.01717, 2018. 6

    2018 arXiv

  24. [32]

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

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

    2025

  25. [33]

    Ibr- net: Learning multi-view image-based rendering

    Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul P Srinivasan, Howard Zhou, Jonathan T Barron, Ricardo Martin-Brualla, Noah Snavely, and Thomas Funkhouser. Ibr- net: Learning multi-view image-based rendering. In Pro- ceedings of the IEEE/CVF conference on computer vision and ...

    2021

  26. [34]

    Dust3r: Geometric 3d vi- sion made easy

    Shuzhe Wang, Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Geometric 3d vi- sion made easy. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20697– 20709, 2024. 1, 2, 3, 4, 5, 6, 8

    2024

  27. [35]

    Image quality assessment: from error visibility to structural similarity

    Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Si- moncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600–612, 2004. 5

    2004

  28. [36]

    Motionctrl: A unified and flexible motion controller for video generation

    Zhouxia Wang, Ziyang Yuan, Xintao Wang, Yaowei Li, Tianshui Chen, Menghan Xia, Ping Luo, and Ying Shan. Motionctrl: A unified and flexible motion controller for video generation. In ACM SIGGRAPH 2024 Conference Pa- pers, pages 1–11, 2024. 3, 5, 6, 7

    2024

  29. [37]

    Synsin: End-to-end view synthesis from a sin- gle image

    Olivia Wiles, Georgia Gkioxari, Richard Szeliski, and Justin Johnson. Synsin: End-to-end view synthesis from a sin- gle image. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7467–7477,

  30. [38]

    ifusion: Inverting diffusion for pose- free reconstruction from sparse views

    Chin-Hsuan Wu, Yen-Chun Chen, Bolivar Solarte, Lu Yuan, and Min Sun. ifusion: Inverting diffusion for pose- free reconstruction from sparse views. arXiv preprint arXiv:2312.17250, 2023. 3

    2023 arXiv

  31. [39]

    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

  32. [40]

    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 Eu- ropean Conference on Computer Vision , pages 399–417. Springer, 2025. 3, 4

    2025

  33. [41]

    Sparp: Fast 3d object reconstruction and pose estimation from sparse views

    Chao Xu, Ang Li, Linghao Chen, Yulin Liu, Ruoxi Shi, Hao Su, and Minghua Liu. Sparp: Fast 3d object reconstruction and pose estimation from sparse views. InEuropean Confer- ence on Computer Vision, pages 143–163. Springer, 2025. 1, 2

    2025

  34. [42]

    pixelnerf: Neural radiance fields from one or few images

    Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelnerf: Neural radiance fields from one or few images. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 4578–4587, 2021. 1

    2021

  35. [43]

    Viewcrafter: Taming video diffusion models for high-fidelity novel view synthesis.arXiv preprint arXiv:2409.02048, 2024

    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. 1, 2, 3, 4, 5, 6

    2024 arXiv

  36. [44]

    Jacobs, Yael Pritch, Inbar Mosseri, Mike Zheng Shou, Neal Wadhwa, and Nataniel Ruiz

    David Junhao Zhang, Roni Paiss, Shiran Zada, Nikhil Karnad, David E. Jacobs, Yael Pritch, Inbar Mosseri, Mike Zheng Shou, Neal Wadhwa, and Nataniel Ruiz. Re- capture: Generative video camera controls for user-provided videos using masked video fine-tuning. arXiv preprint arXiv...

    2024 arXiv

  37. [45]

    Cameras as rays: Pose estimation via ray diffusion

    Jason Y Zhang, Amy Lin, Moneish Kumar, Tzu-Hsuan Yang, Deva Ramanan, and Shubham Tulsiani. Cameras as rays: Pose estimation via ray diffusion. arXiv preprint arXiv:2402.14817, 2024. 8

    2024 arXiv

  38. [46]

    The unreasonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shecht- man, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 586–595, 2018. 5

    2018

  39. [47]

    Stereo magnification: Learning view 10 synthesis using multiplane images

    Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view 10 synthesis using multiplane images. ACM Trans. Graph, 37,

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.