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REVIEW 4 major objections 8 minor 3 cited by

LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors

T0 review · 4 major / 8 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read One photo becomes a 3D Gaussian scene by generating and matching a short video tour around it.

desk verdict LiftImage3D is a credible engineering recipe, but its SOTA numbers are conditional until the test-time pose optimization in Sec. 4.2 is made symmetric with the baselines. read the letter →

arxiv 2412.09597 v1 pith:TQSYSDK2 submitted 2024-12-12 cs.CV cs.GR

Yabo Chen , Chen Yang , Jiemin Fang , Xiaopeng Zhang , Lingxi Xie , Wei Shen , Wenrui Dai , Hongkai Xiong
show 1 more author
Qi Tian
This is my paper · ORCID
classification cs.CVcs.GR
keywords single-image3DreconstructionvideodiffusionpriorsGaussianSplattingdistortion-awarescenerepresentationneuralmatchingnovelviewsynthesiscameraposeestimationdepthpriorinjection
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

LiftImage3D claims that a single photograph can be turned into a 3D Gaussian scene by using a motion-controllable video diffusion model to hallucinate surrounding views, then reconstructing from those generated frames rather than from the input image alone. The paper's argument is that the two forces that wreck this recipe, quality collapse under large camera motion and per-frame geometric distortions from diffusion, can be removed by generating along small articulated trajectory steps and by explicitly modeling each frame's distortion on top of a canonical 3D Gaussian scene. The paper reports higher PSNR than the compared single-image 3D baselines on LLFF, DL3DV, and Tanks and Temples, and the same pipeline improves two different video-generation backbones. In plain terms: generate a short video tour around the image, register the frames into a point cloud by dense matching, absorb the diffusion warps, and keep the clean canonical 3D scene.

What carries the argument

The carrying mechanism is a canonical-distortion split for 3D Gaussian Splatting, a scene rendered as a cloud of ellipsoidal Gaussian primitives. A distortion field network maps each frame's Gaussians back to the canonical scene through per-Gaussian offsets in position, rotation, and scale; the offsets are decoded from a 5D HexPlane/K-Planes factorized feature grid that combines $xyz$ spatial coordinates with a two-dimensional time stamp $(t_i, t_j)$ tracking the left-right and up-down generation directions. The loss includes a term forcing the distortion to zero at the input image's stamp, so after training the offsets are discarded and only canonical Gaussians remain. Supporting mechanisms are the articulated trajectory strategy, which cascades small generated clips to cover wide view ranges, and the one-by-one temporal matching chain that reduces dense matching to $L-1$ pairs.

What would settle it

A direct test is a loop-closure experiment: generate a video trajectory whose last frame should return near the input viewpoint, register it with the same one-by-one matching chain, and compare the matched pose of the final frame with the identity pose. If the accumulated pose error is large under mild trajectories, or if matching each frame only to its predecessor gives a point cloud that differs substantially from matching all pairs, the core assumption fails.

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Extended reading notes

Core claim

The central claim is that a latent video diffusion model, used without fine-tuning, is enough as a single-image-to-3D prior once three conditions are met: large camera motions are decomposed into small articulated steps so each generated clip stays stable; the generated frames' camera poses and coarse point clouds are recovered by dense neural matching instead of by trusting the generator's camera conditioning; and a distortion-aware 3D Gaussian Splatting field absorbs the per-frame geometric warps, leaving an undistorted canonical scene. On LLFF the paper reports a PSNR of 18.24 against 13.51 for ViewCrafter, on DL3DV 21.08 against 17.16, and on Tanks and Temples 16.01 against 13.33; the same pipeline also raises PSNR on DL3DV from 17.00 to 21.08 with MotionCtrl and from 17.16 to 22.66 with ViewCrafter as the backbone.

Load-bearing premise

The load-bearing premise is that consecutive frames generated by the video model overlap enough for the matching step to lock them into one consistent 3D layout, even though each frame carries its own diffusion-induced warp; if one clip drifts or changes appearance, the error flows through the whole chain into the final scene.

Editorial extensions

If this is right

  • A single input image can produce a renderable 3D Gaussian scene with wide viewpoint coverage, not just small-baseline parallax, because the articulated trajectory strategy extends the generated views in multiple directions.
  • The video generator does not need to be fine-tuned for multi-view consistency: the pipeline works by registering whatever frames the generator produces and by modeling their distortions.
  • The paper shows the pipeline works with at least two different video-generation backbones, improving DL3DV PSNR from 17.00 to 21.08 with MotionCtrl and from 17.16 to 22.66 with ViewCrafter.
  • The final representation is canonical and clean: the per-frame distortion field is thrown away after training, so rendering does not inherit the diffusion artifacts.
  • Depth quality improves without extra 3D supervision by calibrating coarse absolute matching depth with fine monocular depth through a median scale-and-shift alignment.

Reading between the lines

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

  • The temporal matching chain is the least costly part of the pipeline but also the least validated; a loop-closure experiment that generates a trajectory returning near the input image would reveal how much pose drift accumulates, and that drift would directly bound the quality of any lifted scene.
  • The evaluation protocol optimizes poses of the test views after training, so the reported numbers include an alignment safety net that would not be present when turning a brand-new photo into a scene; a deployment-oriented benchmark should measure the pipeline without that step.
  • The two-axis stamp is tailored to trajectories generated along up/down/left/right directions; extending the same distortion-field idea to 360-degree orbits or free camera paths would require a spherical or three-axis parameterization, which the current design does not cover.
  • Because the distortion field already separates per-frame variation from the canonical scene, the same representation could plausibly model appearance drift, such as lighting or color shifts across generated frames, not just geometric warp.
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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

4 major / 8 minor

Summary. The paper proposes LiftImage3D, a pipeline for lifting a single image into a 3D Gaussian scene by generating multiple video clips from a frozen motion-controllable latent video diffusion model (MotionCtrl or ViewCrafter) along articulated trajectories, registering the generated frames with MASt3R, and optimizing a distortion-aware 3D Gaussian representation. The distortion field is modeled with a 5D K-Planes/HexPlane encoder and a small MLP, so that per-frame diffusion distortions are separated from an undistorted canonical set of Gaussians. Depth priors from monocular depth are calibrated against MASt3R's coarse absolute depth. The paper reports state-of-the-art PSNR/SSIM on LLFF, DL3DV, and Tanks and Temples, and shows qualitative in-the-wild examples. The central claim is that a video diffusion prior can be lifted into a 3D representation without fine-tuning the generator, provided the generated frames are robustly registered and distortions explicitly modeled.

Significance. If the quantitative results hold under a symmetric evaluation protocol, this would be a useful contribution: it demonstrates that a frozen motion-controllable LVDM can act as a single-image-to-3D prior, and the explicit canonical/distortion decomposition is a sensible way to handle diffusion-induced inconsistencies. The paper ships no code or proofs, but the method is clearly described and the three components (articulated trajectories, MASt3R-based registration, distortion-aware 3DGS) are well motivated. The main value is empirical, so the evaluation protocol is decisive. The current protocol is asymmetric and the LPIPS numbers in Tables 1 and 2 are internally inconsistent with the claimed perceptual superiority; the claim of state-of-the-art performance is therefore conditional on fixing these evaluation issues.

major comments (4)
  1. [Section 4.2, Table 1] The evaluation is asymmetric: the paper states that after training, "we maintain the 3D Gaussians model trained on training views in a frozen state while optimizing the camera poses for evaluation views ... minimizing the photometric discrepancies between the synthesized images and the actual test views." This is a test-time adaptation that uses the ground-truth test images to fit the query poses for LiftImage3D, and Table 1 does not indicate that AdaMPI, SinMPI, LucidDreamer, or ViewCrafter receive the same optimization. Consequently, the reported PSNR gaps (4.73 on LLFF, 3.92 on DL3DV, 2.69 on Tanks and Temples) cannot be attributed solely to the proposed representation; pose fitting alone could account for a large part of the difference. Please report results without test-view pose optimization, or apply the same pose optimization to all baselines.
  2. [Table 1 and Table 2, DL3DV rows] The LPIPS numbers undermine the claim of consistent improvement. On DL3DV, LiftImage3D-MotionCtrl has LPIPS 0.451, which is worse than both the MotionCtrl baseline (0.371) and ViewCrafter (0.407); only LiftImage3D-ViewCrafter improves over ViewCrafter (0.387). Since Table 2 is generated with the same evaluation protocol as Table 1, the proposed distortion-aware 3DGS is not uniformly beneficial on this perceptual metric. The paper should explain this pattern; otherwise "higher visual quality and better 3D consistency" is only supported by PSNR/SSIM, which are precisely the metrics most likely to be inflated by test-time pose fitting.
  3. [Section 4.1 and Table 1] The paper says it randomly selected 20 LLFF scenes, 20 DL3DV scenes, and all Tanks and Temples test sets, but does not state whether the baseline numbers in Table 1 were obtained by re-running the baselines on this same subset or copied from the original papers. If the baseline numbers come from different scene subsets, the comparison is invalid. Please clarify the exact protocol and, if necessary, re-run all methods on the identical views.
  4. [Section 3.4] The registration step is a load-bearing part of the pipeline, but it is not validated in isolation. MASt3R is run only on L-1 temporal pairs (each frame with its immediate predecessor), so any drift or appearance change in the generated video can silently corrupt the merged point cloud, and this corruption propagates into the 3D Gaussian optimization. The paper provides no quantitative evaluation of pose accuracy or point-cloud consistency against the COLMAP ground truth available in LLFF, DL3DV, and Tanks and Temples. The test-time pose optimization in Section 4.2 is not available when lifting a new image, so the matching quality should be characterized on the generated frames, for example with pose error or reprojection error, ideally compared against dense pairwise matching.
minor comments (8)
  1. [Abstract] The abstract says "state-of-the-art performance on two challenging datasets" but then lists three datasets (LLFF, DL3DV, and Tanks and Temples); please correct this.
  2. [References [1]] Reference [1] is cited for Stable Video Diffusion but is titled "Text-to-3d using Gaussian Splatting"; the SVD paper should be cited instead.
  3. [Eq. (4)] The frame count is unclear: the text says the second generation stage only has D-1 directions, but then the total L = l*D + (l-1)*(D-1) does not match the indices in the displayed equations; please clarify whether the anchor frame is reused and define l and D unambiguously.
  4. [Eq. (5)] In Eq. (5), O_{i,j} is not defined and the meaning of the Weiszfeld optimization over focal length is not self-contained; please define the confidence/validity mask and state whether all frames share the estimated intrinsics.
  5. [Eq. (8)] The text says bilinear interpolation is used to query voxel features at "5 vertices of the grid", but a 5D grid cell has 2^5 vertices and the interpolation should be multilinear; please correct the description.
  6. [Eq. (9)] Adding a predicted offset directly to a rotation quaternion and a scaling vector is not meaningful without normalization; please specify how r' and s' are normalized and how this affects the Gaussian parameters.
  7. [Section 4.2] The reference to "InstantSplatting [10]" should be "InstantSplat", and the paper should state whether the test-time pose optimization is applied in Tables 2 and 3 as well as Table 1.
  8. [Section 4.3] The generalization claims for in-the-wild images are supported only by qualitative examples; a quantitative evaluation or a user study would strengthen the claim.

Circularity Check

1 steps flagged · score 6.0 of 10

Headline PSNR gains are partially circular: evaluation-view poses are fitted by minimizing photometric error against the same test views used to compute Table 1, with no reported symmetric pose fitting for baselines.

  1. fitted input called prediction [Section 4.2 (Implementation Details), used in Table 1]
    "For the evaluation, all the camera poses and trajectories are estimated via MASt3R, which may not be well aligned with the ground truth. Therefore, we maintain the 3D Gaussians model trained on training views in a frozen state while optimizing the camera poses for evaluation views, following the setting of InstantSplatting [10] and NeRFmm [66]. This optimization process focuses on minimizing the photometric discrepancies between the synthesized images and the actual test views, aiming to achieve a more precise alignment for fair comparisons."

    The evaluation-view camera pose is a free parameter optimized by minimizing the photometric discrepancy between synthesized images and the actual test views, and the same test views are then used to compute the PSNR values in Table 1. PSNR is a direct function of that photometric discrepancy, so the reported numbers are partially fitted rather than predicted. The paper does not state that the baselines (AdaMPI, SinMPI, LucidDreamer, ViewCrafter) receive the same pose refinement, making the SOTA comparison asymmetric. The 3D Gaussians themselves stay frozen, so the method is not fully refit to the test views, but the headline PSNR advantage is not an independent held-out measurement.

full rationale

The forward pipeline (articulated LVDM generation, MASt3R matching and registration, and distortion-aware 3DGS optimization) is internally consistent: the canonical Gaussians are optimized against the generated frames and input image, with Ldistort acting as a regularizer and the calibrated depth as a self-supervision signal rather than as an evaluation target. I find no self-definitional equations and no load-bearing self-citation chain: the citations to [80] and [74] are accompanied by independent external references and are used only for generic 3DGS and deformation-field motivation. The one substantive circular step is in the evaluation protocol: Section 4.2 fits evaluation-view poses by minimizing photometric error against the test views, and Table 1 reports photometric error (PSNR) on those same views. This means the reported SOTA numbers are partly optimized rather than predicted, and the comparison with baselines is asymmetric. The DL3DV LPIPS result (LiftImage3D 0.451 versus ViewCrafter 0.407) is consistent with PSNR being inflated by pose alignment rather than by overall reconstruction fidelity. Because the model parameters are not refit, this is partial, not total, circularity; the method itself still has independent content.

Assumptions & free parameters 5 free parameters · 5 assumptions · 1 invented entities

The central claim rests on several assumptions that are not independently verified: the usefulness of LVDM priors for arbitrary input images, the reliability of MASt3R on generated frames, the capacity of the distortion field, and the affine depth calibration. Several low-dimensional parameters (loss weights, scale/shift calibration, focal length, evaluation poses) are fitted to data or chosen by hand and are not all reported. No new physical entities are introduced beyond a learned network module.

free parameters (5)
  • test-time evaluation camera poses = optimized per test view via photometric loss
    Section 4.2 freezes the trained 3D Gaussians and optimizes the pose of each evaluation view against the ground-truth test image. This post-hoc fitting can inflate PSNR/SSIM and is not applied to the published baseline numbers.
  • depth scale and shift (Scale, Shift) = per-image medians in Eq. (10)
    Used to calibrate Depth Anything V2 relative depth to MASt3R absolute depth. The resulting depth prior directly supervises the 3DGS optimization; the affine calibration is fitted to each image pair.
  • focal length f* = Weiszfeld optimum per scene (Eq. 5)
    A single shared focal length is estimated from point maps; all frames are assumed to share intrinsics.
  • loss weights for L1RGB, LPIPS, L1depth, Ltv, Ldistort = not reported
    The total loss in Eq. (12) combines five terms; the coefficients are chosen by hand but their values are not listed, making exact replication impossible.
  • number of motion directions D and clip length = D=4; 16 frames for MotionCtrl, 25 for ViewCrafter
    Articulated trajectory hyperparameters chosen by hand; they control the trade-off between view coverage and generation quality.
assumptions (5)
  • domain assumption LVDMs such as SVD/MotionCtrl/ViewCrafter contain transferable 3D priors that can be steered to produce useful novel views of a single input image.
    Introduced in Section 1 and Section 3.3; if the video generator fails for an input, the entire pipeline degrades or collapses.
  • domain assumption MASt3R point-map matching with temporal-next pairing (L-1 pairs) is sufficient to register the generated frames into a coherent point cloud.
    Section 3.4 replaces full pairwise matching with one-pair-per-frame to cut compute; this assumes consecutive frames overlap enough and that the neural matcher is robust to diffusion distortions.
  • ad hoc to paper The distortion field F, implemented as a 9-plane 5D K-Planes/HexPlane encoder plus a tiny MLP, can represent the per-frame distortions accurately enough for the canonical 3DGS to be clean.
    Section 3.5; no external validation of the deformation field's capacity or smoothness. The paper's own 'w/o distortion-field' and 'w/o Ldistort' ablations show performance is sensitive to this component.
  • domain assumption Depth Anything V2's relative depth is affinely related to MASt3R's absolute depth per image, so a single median-based scale and shift can reconcile them.
    Eq. (10) in Section 3.5; scenes with large depth discontinuities or occlusions violate the global affine assumption.
  • standard math Procrustes alignment and the Weiszfeld algorithm converge to the intended optima for pose and focal-length estimation.
    Used in Sections 3.4 and 3.1; assumed as background, with no convergence guarantees discussed for noise-laden data.
invented entities (1)
  • distortion field network F (canonical-to-distortion mapping)
    purpose: Maps canonical 3D Gaussians plus a 2D time stamp to per-frame offsets in position, rotation, and scale, so that generated frames can be explained without corrupting the canonical scene (Eqs. 7-9).
    This is an internal learned module, not a physical entity; its utility is demonstrated only inside the paper's own ablations (Table 3) and it has no falsifiable external handle.

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Cite this review

Pith. "Pith review of LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors." pith.science (2026). https://pith.science/paper/TQSYSDK2

@misc{pith2026241209597,
  author       = {Pith},
  title        = {Pith review of: LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TQSYSDK2}},
  note         = {Machine review of arXiv:2412.09597}
}
read the original abstract

Single-image 3D reconstruction remains a fundamental challenge in computer vision due to inherent geometric ambiguities and limited viewpoint information. Recent advances in Latent Video Diffusion Models (LVDMs) offer promising 3D priors learned from large-scale video data. However, leveraging these priors effectively faces three key challenges: (1) degradation in quality across large camera motions, (2) difficulties in achieving precise camera control, and (3) geometric distortions inherent to the diffusion process that damage 3D consistency. We address these challenges by proposing LiftImage3D, a framework that effectively releases LVDMs' generative priors while ensuring 3D consistency. Specifically, we design an articulated trajectory strategy to generate video frames, which decomposes video sequences with large camera motions into ones with controllable small motions. Then we use robust neural matching models, i.e. MASt3R, to calibrate the camera poses of generated frames and produce corresponding point clouds. Finally, we propose a distortion-aware 3D Gaussian splatting representation, which can learn independent distortions between frames and output undistorted canonical Gaussians. Extensive experiments demonstrate that LiftImage3D achieves state-of-the-art performance on two challenging datasets, i.e. LLFF, DL3DV, and Tanks and Temples, and generalizes well to diverse in-the-wild images, from cartoon illustrations to complex real-world scenes.

Figures

Figures reproduced from arXiv: 2412.09597 by the authors.

Figure 1
Figure 1. LiftImage3D is a universal framework that utilizes video generation priors to lift any single 2D image into 3D Gaussians, capable [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The overall pipeline of LiftImage3D. We first extend LVDM to generate multiple video clips from a single image using an [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Articulated trajectory generation pipeline. The gray [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: The overall qualitative results of our methods compared with AdaMPI [ [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Visualization of proposed depth prior injection. The first column lays the video frames generated by LVDM or input images. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

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Reference graph

Works this paper leans on

92 extracted references · 30 canonical work pages · cited by 3 Pith papers

  1. [1]

    Blattmann, T

    A. Blattmann, T. Dockhorn, S. Kulal, D. Mendelevitch, M. Kilian, and D Lorenz. Text-to-3d using gaussian splatting. arXiv:2311.15127, 2023. 3

    arXiv 2023

  2. [2]

    Hexplane: A fast representa- tion for dynamic scenes

    Ang Cao and Justin Johnson. Hexplane: A fast representa- tion for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 130–141, 2023. 2, 6

    2023

  3. [3]

    Chan, Koki Nagano, Matthew A

    Eric R. Chan, Koki Nagano, Matthew A. Chan, Alexan- der W. Bergman, Jeong Joon Park, Axel Levy, Miika Ait- tala, Shalini De Mello, Tero Karras, and Gordon Wetzstein. GeNVS: Generative novel view synthesis with 3D-aware dif- fusion models. In arXiv, 2023. 3

    2023

  4. [4]

    Passthrough+ real-time stereoscopic view synthesis for mo- bile mixed reality

    Gaurav Chaurasia, Arthur Nieuwoudt, Alexandru-Eugen Ichim, Richard Szeliski, and Alexander Sorkine-Hornung. Passthrough+ real-time stereoscopic view synthesis for mo- bile mixed reality. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 3(1):1–17, 2020. 3

    2020

  5. [5]

    Single-view 3D Scene Reconstruction with High-fidelity Shape and Texture

    Yixin Chen, Junfeng Ni, Nan Jiang, Yaowei Zhang, Yixin Zhu, and Siyuan Huang. Single-view 3d scene reconstruc- tion with high-fidelity shape and texture. arXiv:2311.00457,

    work page Pith review arXiv

  6. [6]

    Extreme view synthesis

    Inchang Choi, Orazio Gallo, Alejandro Troccoli, Min H Kim, and Jan Kautz. Extreme view synthesis. InProceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 7781–7790, 2019. 3

    2019

  7. [7]

    Luciddreamer: Domain-free gen- eration of 3d gaussian splatting scenes

    Jaeyoung Chung, Suyoung Lee, Hyeongjin Nam, Jaerin Lee, and Kyoung Mu Lee. Luciddreamer: Domain-free gen- eration of 3d gaussian splatting scenes. arXiv preprint arXiv:2311.13384, 2023. 2, 3, 7, 8

    arXiv 2023

  8. [8]

    Objaverse-xl: A universe of 10m+ 3d objects

    Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Chris- tian Laforte, Vikram V oleti, Samir Yitzhak Gadre, Eli VanderBilt, Aniruddha Kembhavi, Carl V ondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, and Ali Farhadi. Objaverse-xl: A universe of 10m+ 3d objects. arXiv preprint arXiv:2307.05663, 2023. 3

    arXiv 2023

Show all 92 references
  1. [9]

    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 CVPR, pages 13142– 13153, 2023. 3

    2023

  2. [10]

    Instantsplat: Unbounded sparse-view pose-free gaus- sian splatting in 40 seconds, 2024

    Zhiwen Fan, Wenyan Cong, Kairun Wen, Kevin Wang, Jian Zhang, Xinghao Ding, Danfei Xu, Boris Ivanovic, Marco Pavone, Georgios Pavlakos, Zhangyang Wang, and Yue Wang. Instantsplat: Unbounded sparse-view pose-free gaus- sian splatting in 40 seconds, 2024. 7

    2024

  3. [11]

    K-planes: Explicit radiance fields in space, time, and appearance

    Sara Fridovich-Keil, Giacomo Meanti, Frederik Rahbæk Warburg, Benjamin Recht, and Angjoo Kanazawa. K-planes: Explicit radiance fields in space, time, and appearance. In CVPR, 2023. 6

    2023

  4. [12]

    SinMPI: Novel view synthesis from a single image with expanded multiplane images

    Pu Guo, Peng-Shuai Wang, and Zhouhui Lian. SinMPI: Novel view synthesis from a single image with expanded multiplane images. ACM Transactions on Graphics (SIG- GRAPH Asia), 42(6), 2023. 2, 7, 8

    2023

  5. [13]

    SinMPI: Novel view synthesis from a single image with expanded multiplane images

    Pu Guo, Peng-Shuai Wang, and Zhouhui Lian. SinMPI: Novel view synthesis from a single image with expanded multiplane images. In SIGGRAPH Asia 2023 Conference Papers, pages 1–9. ACM, 2023. 3

    2023

  6. [14]

    Sparf: Large-scale learning of 3d sparse radiance fields from few input images

    Abdullah Hamdi, Bernard Ghanem, and Matthias Nießsner. Sparf: Large-scale learning of 3d sparse radiance fields from few input images. In ICCV, pages 2930–2940, 2023. 3

    2023

  7. [15]

    VFusion3D: Learning scalable 3D generative models from video diffu- sion models

    Junlin Han, Filippos Kokkinos, and Philip Torr. VFusion3D: Learning scalable 3D generative models from video diffu- sion models. arXiv preprint arXiv:2403.12034, 2024. 2, 3

    2024 arXiv

  8. [16]

    Single- view view synthesis in the wild with learned adaptive multi- plane images

    Yuxuan Han, Ruicheng Wang, and Jiaolong Yang. Single- view view synthesis in the wild with learned adaptive multi- plane images. In ACM SIGGRAPH, 2022. 2, 3, 7, 8

    2022

  9. [17]

    Human- liff: Layer-wise 3d human generation with diffusion model

    Shoukang Hu, Fangzhou Hong, Tao Hu, Liang Pan, Haiyi Mei, Weiye Xiao, Lei Yang, and Ziwei Liu. Human- liff: Layer-wise 3d human generation with diffusion model. arXiv:2308.09712, 2023. 3

    2023 arXiv

  10. [18]

    Zixuan Huang, Stefan Stojanov, Anh Thai, Varun Jampani, and James M. Rehg. Zeroshape: Regression-based zero-shot shape reconstruction. arXiv:2312.14198, 2024. 3

    2024 arXiv

  11. [19]

    Heewoo Jun and Alex. et al. Nichol. Shap-e: Generating conditional 3d implicit functions. arXiv:2305.02463, 2023. 3

    2023 arXiv

  12. [20]

    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 Transactions on Graphics ,

  13. [21]

    Stereo magnification with multi-layer images

    Taras Khakhulin, Denis Korzhenkov, Pavel Solovev, Gleb Sterkin, Andrei-Timotei Ardelean, and Victor Lempitsky. Stereo magnification with multi-layer images. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8687–8696, 2022. 3

    2022

  14. [22]

    Tiled mul- tiplane images for practical 3d photography

    Khan, Numair, Lei Xiao, and Douglas Lanman. Tiled mul- tiplane images for practical 3d photography. In ICCV, 2023. 2

    2023

  15. [23]

    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, 36(4), 2017. 2, 7

    2017

  16. [24]

    In- trinsic image diffusion for single-view material estimation

    Peter Kocsis, Vincent Sitzmann, and Matthias Nießner. In- trinsic image diffusion for single-view material estimation. arXiv:2312.12274, 2023. 3

    2023 arXiv

  17. [25]

    Vivid-1-to-3: Novel view synthesis with video diffusion models

    Jun-Gyu Kwak, Eunhyeok Dong, Youngho Jin, Hosik Ko, Surbhi Mahajan, and Kwang Moo Yi. Vivid-1-to-3: Novel view synthesis with video diffusion models. arXiv preprint arXiv:2312.01305, 2023. 2, 3

    2023 arXiv

  18. [26]

    D. Lee, C. Kim, M. Cho, and W. S. Han. Locality- aware generalizable implicit neural representation. In arXiv:2310.05624, 2023. 3

    2023 arXiv

  19. [27]

    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. 2, 4, 5, 7

    2024 arXiv

  20. [28]

    Omnisyn: Synthesizing 360 videos with wide-baseline panoramas

    David Li, Yinda Zhang, Christian H ¨ane, Danhang Tang, Amitabh Varshney, and Ruofei Du. Omnisyn: Synthesizing 360 videos with wide-baseline panoramas. In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Ab- stracts and Workshops (VRW), pages 670–671. IEEE, 2022. 3

    2022

  21. [29]

    Synthesizing light field from a single image with variable mpi and two network fusion

    Qinbo Li and Nima Khademi Kalantari. Synthesizing light field from a single image with variable mpi and two network fusion. ACM Trans. Graph., 39(6):229–1, 2020. 3

    2020

  22. [30]

    Zanjani, Haitam Ben Yahia, Yuki M

    Shijie Li, Farhad G. Zanjani, Haitam Ben Yahia, Yuki M. Asano, Juergen Gall, and Amirhossein Habibian. Valid: Variable-length input diffusion for novel view synthesis. arXiv:2312.08892, 2023. 3

    2023 arXiv

  23. [31]

    Consistent123: One image to highly consistent 3d asset using case-aware diffusion priors

    Yukang Lin, Haonan Han, Chaoqun Gong, Zunnan Xu, Yachao Zhang, and Xiu Li. Consistent123: One image to highly consistent 3d asset using case-aware diffusion priors. arXiv:2309.17261, 2023. 3

    2023 arXiv

  24. [32]

    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

  25. [33]

    One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion

    Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Ji- ayuan Gu, and Hao Su. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. arXiv:2311.07885, 2023. 3

    2023 arXiv

  26. [34]

    One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization

    Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Zexi- ang Xu, Hao Su, et al. One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization. arXiv:2306.16928, 2023. 3

    2023 arXiv

  27. [35]

    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. arXiv:2303.11328, 2023

    2023 arXiv

  28. [36]

    Syncdreamer: Gen- erating multiview-consistent images from a single-view im- age

    Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. Syncdreamer: Gen- erating multiview-consistent images from a single-view im- age. arXiv:2309.03453, 2023. 3

    2023 arXiv

  29. [37]

    Y . Liu, K. Zhang, Y . Li, Z. Yan, C. Gao, and R. Chen. Sora: A review on background, technology, limitations, and opportunities of large vision models. arXiv preprint arXiv:2402.17177, 2024. 3

    2024 arXiv

  30. [38]

    Won- der3d: Single 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. Won- der3d: Single image to 3d using cross-domain diffusion. arXiv:2310.15008, 2023. 3

    arXiv

  31. [39]

    Bin Luo and Edwin R. Hancock. Procrustes alignment with the em algorithm. International Conference on Computer Analysis of Images and Patterns., 1999. 4, 5

    1999

  32. [40]

    Adaptive multiplane image gen- eration from a single internet picture

    Diogo C Luvizon, Gustavo Sutter P Carvalho, Andreza A dos Santos, Jhonatas S Conceicao, Jose L Flores-Campana, Luis GL Decker, Marcos R Souza, Helio Pedrini, Antonio Joia, and Otavio AB Penatti. Adaptive multiplane image gen- eration from a single internet picture. In Proceedi...

    2021

  33. [41]

    Realfusion: 360deg reconstruction of any object from a single image

    Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, and Andrea Vedaldi. Realfusion: 360deg reconstruction of any object from a single image. In CVPR, pages 8446–8455,

  34. [42]

    IM-3D: Iterative multiview diffusion and recon- struction for high-quality 3D generation

    Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Na- talia Neverova, Andrea Vedaldi, Oran Gafni, and Filippos Kokkinos. IM-3D: Iterative multiview diffusion and recon- struction for high-quality 3D generation. arXiv preprint arXiv:2402.08682, 2024. 2, 3

    2024 arXiv

  35. [43]

    Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar

    Ben Mildenhall, Pratul P. Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar. Local light field fusion: Practical view syn- thesis with prescriptive sampling guidelines. ACM Transac- tions on Graphics (TOG), 2019. 2, 7

    2019

  36. [44]

    Point-e: A system for generating 3d point clouds from complex prompts

    Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. Point-e: A system for generating 3d point clouds from complex prompts. arXiv:2212.08751,

    arXiv

  37. [45]

    Reg- nerf: Regularizing neural radiance fields for view synthesis from sparse inputs

    Michael Niemeyer, Jonathan T Barron, Ben Mildenhall, Mehdi SM Sajjadi, Andreas Geiger, and Noha Radwan. Reg- nerf: Regularizing neural radiance fields for view synthesis from sparse inputs. arXiv preprint arXiv:2112.00724, 2021. 7

    2021 arXiv

  38. [46]

    3d ken burns effect from a single image

    Simon Niklaus, Long Mai, Jimei Yang, and Feng Liu. 3d ken burns effect from a single image. ACM Transactions on Graphics (ToG), 38(6):1–15, 2019. 3

    2019

  39. [47]

    Envision3D: One im- age to 3D with anchor views interpolation

    Yiyu Pang, Tong Jia, Yichun Shi, Zimeng Tang, Jiaxi- ang Zhang, and Xiaohua Cheng. Envision3D: One im- age to 3D with anchor views interpolation. arXiv preprint arXiv:2403.08902, 2024. 2, 3 11

    2024 arXiv

  40. [48]

    Plastria

    Frank. Plastria. The weiszfeld algorithm: proof, amend- ments, and extensions. Foundations of location analysis ,

  41. [49]

    Magic123: One image to high-quality 3d object generation using both 2d and 3d diffusion priors

    Guocheng Qian, Jinjie Mai, Abdullah Hamdi, Jian Ren, Aliaksandr Siarohin, Bing Li, Hsin-Ying Lee, Ivan Sko- rokhodov, Peter Wonka, Sergey Tulyakov, et al. Magic123: One image to high-quality 3d object generation using both 2d and 3d diffusion priors. arXiv:2306.17843, 2023. 3

    2023 arXiv

  42. [50]

    Pushing auto- regressive models for 3d shape generation at capacity and scalability

    Xuelin Qian, Yu Wang, Simian Luo, Yinda Zhang, Ying Tai, Zhenyu Zhang, Chengjie Wang, Xiangyang Xue, Bo Zhao, Tiejun Huang, Yunsheng Wu, and Yanwei Fu. Pushing auto- regressive models for 3d shape generation at capacity and scalability. arXiv:2402.12225, 2024. 3

    2024 arXiv

  43. [51]

    Zeronvs: Zero- shot 360-degree view synthesis from a single real image

    Kyle Sargent, Zizhang Li, Tanmay Shah, Charles Herrmann, Hong-Xing Yu, Yunzhi Zhang, Eric Ryan Chan, Dmitry Lagun, Li Fei-Fei, Deqing Sun, et al. Zeronvs: Zero- shot 360-degree view synthesis from a single real image. arXiv:2310.17994, 2023. 3

    arXiv

  44. [52]

    Structure-from-motion revisited

    Johannes Lutz Sch ¨onberger and Jan-Michael Frahm. Structure-from-motion revisited. In Conference on Com- puter Vision and Pattern Recognition (CVPR), 2016. 5

    2016

  45. [53]

    Pixelwise view selection for un- structured multi-view stereo

    Johannes Lutz Sch ¨onberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm. Pixelwise view selection for un- structured multi-view stereo. In European Conference on Computer Vision (ECCV), 2016. 5

    2016

  46. [54]

    Anything- 3d: Towards single-view anything reconstruction in the wild

    Qiuhong Shen, Xingyi Yang, and Xinchao Wang. Anything- 3d: Towards single-view anything reconstruction in the wild. arXiv:2304.10261, 2023. 3

    2023 arXiv

  47. [55]

    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:2310.15110, 2023

    2023 arXiv

  48. [57]

    Toss:high-quality text-guided novel view synthesis from a single image

    Yukai Shi, Jianan Wang, He Cao, Boshi Tang, Xianbiao Qi, Tianyu Yang, Yukun Huang, Shilong Liu, Lei Zhang, and Heung-Yeung Shum. Toss:high-quality text-guided novel view synthesis from a single image. arXiv:2310.10644,

    arXiv

  49. [58]

    3d photography using context-aware layered depth inpainting

    Meng-Li Shih, Shih-Yang Su, Johannes Kopf, and Jia-Bin Huang. 3d photography using context-aware layered depth inpainting. In IEEE Conference on Computer Vision and Pat- tern Recognition (CVPR), 2020. 3

    2020

  50. [59]

    Hyper- voltran: Fast and generalizable one-shot image to 3d object structure via hypernetworks

    Christian Simon, Sen He, Juan-Manuel Perez-Rua, Meng- meng Xu, Amine Benhalloum, and Tao Xiang. Hyper- voltran: Fast and generalizable one-shot image to 3d object structure via hypernetworks. arXiv:2312.16218, 2024. 3

    2024 arXiv

  51. [60]

    Pushing the boundaries of view extrapolation with multiplane images

    Pratul P Srinivasan, Richard Tucker, Jonathan T Barron, Ravi Ramamoorthi, Ren Ng, and Noah Snavely. Pushing the boundaries of view extrapolation with multiplane images. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 175–184, 2019. 2

    2019

  52. [61]

    Stereo matching with transparency and matting

    Richard Szeliski and Polina Golland. Stereo matching with transparency and matting. In Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271) , pages 517–524. IEEE, 1998. 2

    1998

  53. [62]

    Make-it-3d: High- fidelity 3d creation from a single image with diffusion prior

    Junshu Tang, Tengfei Wang, Bo Zhang, Ting Zhang, Ran Yi, Lizhuang Ma, and Dong Chen. Make-it-3d: High- fidelity 3d creation from a single image with diffusion prior. arXiv:2303.14184, 2023. 3

    2023 arXiv

  54. [63]

    Mvdiffusion++: A dense high- resolution multi-view diffusion model for single or sparse- view 3d object reconstruction

    Shitao Tang, Jiacheng Chen, Dilin Wang, Chengzhou Tang, Fuyang Zhang, Yuchen Fan, Vikas Chandra, Yasutaka Fu- rukawa, and Rakesh Ranjan. Mvdiffusion++: A dense high- resolution multi-view diffusion model for single or sparse- view 3d object reconstruction. arXiv:2402.12712, 2024. 3

    2024 arXiv

  55. [64]

    SV3D: Novel multi-view synthesis and 3D generation from a sin- gle image using latent video diffusion

    Vikram V oleti, Cheng-Hao Yao, Matthew Boss, Alexan- der Letts, Daniel Pankratz, and Denis Tochilkin. SV3D: Novel multi-view synthesis and 3D generation from a sin- gle image using latent video diffusion. arXiv preprint arXiv:2403.12008, 2024. 2, 3

    2024 arXiv

  56. [65]

    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. In CVPR, 2024. 2, 4

    2024

  57. [66]

    NeRF −−: Neural radiance fields without known camera parameters

    Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, and Victor Adrian Prisacariu. NeRF −−: Neural radiance fields without known camera parameters. arXiv preprint arXiv:2102.07064, 2021. 7

    2021 arXiv

  58. [67]

    Motionctrl: A uni- fied and flexible motion controller for video generation

    Zhouxia Wang, Ziyang Yuan, Xintao Wang, Tianshui Chen, Menghan Xia, Ping Luo, and Yin Shan. Motionctrl: A uni- fied and flexible motion controller for video generation. In arXiv preprint arXiv:2312.03641, 2023. 2, 3, 7, 8, 9

    2023 arXiv

  59. [68]

    Consistent123: Im- prove consistency for one image to 3d object synthesis

    Haohan Weng, Tianyu Yang, Jianan Wang, Yu Li, Tong Zhang, CL Chen, and Lei Zhang. Consistent123: Im- prove consistency for one image to 3d object synthesis. arXiv:2310.08092, 2023. 3

    2023 arXiv

  60. [69]

    Zeroa- vatar: Zero-shot 3d avatar generation from a single image

    Zhenzhen Weng, Zeyu Wang, and Serena Yeung. Zeroa- vatar: Zero-shot 3d avatar generation from a single image. arXiv:2305.16411, 2023. 3

    2023 arXiv

  61. [70]

    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, 2020. 3

    2020

  62. [71]

    Li, x., zhou, d., zhang, c., wei, s., hou, q., and cheng, m

    Sora Generates Videos with Stunning Geometrical Consis- tency. Li, x., zhou, d., zhang, c., wei, s., hou, q., and cheng, m. m. arXiv:2402.17403, 2024. 3

    2024 arXiv

  63. [72]

    Nex: Real-time view synthesis with neural basis expansion

    Suttisak Wizadwongsa, Pakkapon Phongthawee, Jiraphon Yenphraphai, and Supasorn Suwajanakorn. Nex: Real-time view synthesis with neural basis expansion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 8534–8543, 2021. 2

    2021

  64. [73]

    Harmonyview: Harmonizing consis- tency and diversity in one-image-to-3d

    Sangmin Woo, Byeongjun Park, Hyojun Go, Jin-Young Kim, and Changick Kim. Harmonyview: Harmonizing consis- tency and diversity in one-image-to-3d. arXiv:2312.15980,

    arXiv

  65. [74]

    4d gaussian splatting for real-time dynamic scene rendering

    Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Wang Xinggang. 4d gaussian splatting for real-time dynamic scene rendering. arXiv preprint arXiv:2310.08528, 2023. 6

    2023 arXiv

  66. [75]

    Hyperdreamer: 12 Hyper-realistic 3d content generation and editing from a sin- gle image

    Tong Wu, Zhibing Li, Shuai Yang, Pan Zhang, Xingang Pan, Jiaqi Wang, Dahua Lin, and Ziwei Liu. Hyperdreamer: 12 Hyper-realistic 3d content generation and editing from a sin- gle image. In SIGGRAPH Asia 2023 Conference Papers ,

    2023

  67. [76]

    3d-aware image generation using 2d diffusion mod- els

    Jianfeng Xiang, Jiaolong Yang, Binbin Huang, and Xin Tong. 3d-aware image generation using 2d diffusion mod- els. In Proceedings of the IEEE/CVF International Confer- ence on Computer Vision (ICCV) , pages 2383–2393, 2023. 3

    2023

  68. [77]

    Neuralpassthrough: Learned real-time view synthesis for vr

    Lei Xiao, Salah Nouri, Joel Hegland, Alberto Garcia Garcia, and Douglas Lanman. Neuralpassthrough: Learned real-time view synthesis for vr. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1–9, 2022. 3

    2022

  69. [78]

    Sinnerf: Training neural radiance fields on complex scenes from a single image

    Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, and Zhangyang Wang. Sinnerf: Training neural radiance fields on complex scenes from a single image. 2022. 2

    2022

  70. [79]

    Neurallift-360: Lifting an in-the-wild 2d photo to a 3d object with 360deg views

    Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Yi Wang, and Zhangyang Wang. Neurallift-360: Lifting an in-the-wild 2d photo to a 3d object with 360deg views. In CVPR, pages 4479–4489, 2023. 3

    2023

  71. [80]

    Gaussianob- ject: Just taking four images to get a high-quality 3d object with gaussian splatting

    Chen Yang, Sikuang Li, Jiemin Fang, Ruofan Liang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, and Qi Tian. Gaussianob- ject: Just taking four images to get a high-quality 3d object with gaussian splatting. arXiv:2402.10259, 2024. 3

    2024 arXiv

  72. [81]

    Consistnet: Enforcing 3d consistency for multi- view images diffusion

    Jiayu Yang, Ziang Cheng, Yunfei Duan, Pan Ji, and Hong- dong Li. Consistnet: Enforcing 3d consistency for multi- view images diffusion. arXiv:2310.10343, 2023. 3

    2023 arXiv

  73. [82]

    Depth any- thing v2

    Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiao- gang Xu, Jiashi Feng, and Hengshuang Zhao. Depth any- thing v2. arXiv:2406.09414, 2024. 6, 9

    2024 arXiv

  74. [83]

    Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction.arXiv preprint arXiv:2309.13101, 2023

    Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, and Xiaogang Jin. Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction.arXiv preprint arXiv:2309.13101, 2023. 6

    2023 arXiv

  75. [84]

    No pose, no problem: Surprisingly simple 3d gaussian splats from sparse unposed images

    Botao Ye, Sifei Liu, Haofei Xu, Li Xueting, Marc Pollefeys, Ming-Hsuan Yang, and Peng Songyou. No pose, no problem: Surprisingly simple 3d gaussian splats from sparse unposed images. arXiv preprint arXiv:2410.24207, 2024. 3

    2024 arXiv

  76. [85]

    Consistent-1-to-3: Consistent image to 3d view synthesis via geometry-aware diffusion models

    Jianglong Ye, Peng Wang, Kejie Li, Yichun Shi, and Heng Wang. Consistent-1-to-3: Consistent image to 3d view synthesis via geometry-aware diffusion models. arXiv:2310.03020, 2023. 3

    2023 arXiv

  77. [86]

    Nvs-solver: Video diffusion model as zero-shot novel view synthesizer

    Meng You, Zhiyu Zhu, Hui Liu, and Junhui Hou. Nvs-solver: Video diffusion model as zero-shot novel view synthesizer. arXiv preprint arXiv:2405.15364, 2024. 3

    2024 arXiv

  78. [87]

    Wonderworld: Interactive 3d scene generation from a single image

    Hong-Xing Yu, Haoyi Duan, Charles Herrmann, William T Freeman, and Jiajun Wu. Wonderworld: Interactive 3d scene generation from a single image. arXiv preprint arXiv:2406.09394, 2024. 3

    2024 arXiv

  79. [88]

    Boosting3d: High-fidelity image-to-3d by boosting 2d diffusion prior to 3d prior with progressive learn- ing

    Kai Yu, Jinlin Liu, Mengyang Feng, Miaomiao Cui, and Xuansong Xie. Boosting3d: High-fidelity image-to-3d by boosting 2d diffusion prior to 3d prior with progressive learn- ing. arXiv:2311.13617, 2023. 3

    2023 arXiv

  80. [89]

    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. 3, 7, 8, 9

    2024 arXiv

  81. [90]

    Repaint123: Fast and high-quality one image to 3d generation with pro- gressive controllable 2d repainting.arXiv:2312.13271, 2023

    Junwu Zhang, Zhenyu Tang, Yatian Pang, Xinhua Cheng, Peng Jin, Yida Wei, Munan Ning, and Li Yuan. Repaint123: Fast and high-quality one image to 3d generation with pro- gressive controllable 2d repainting.arXiv:2312.13271, 2023. 3

    2023 arXiv

  82. [91]

    Monst3r: A simple approach for estimat- ing geometry in the presence of motion

    Junyi Zhang, Charles Herrmann, Junhwa Hur, Varun Jam- pani, Trevor Darrell, Forrester Cole, Deqing Sun, and Ming- Hsuan Yang. Monst3r: A simple approach for estimat- ing geometry in the presence of motion. arXiv preprint arXiv:2410.03825, 2024. 2, 4

    arXiv

  83. [92]

    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 CVPR, 2018. 7

    2018

  84. [93]

    Stereo magnification: Learning view syn- thesis using multiplane images

    Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view syn- thesis using multiplane images. In SIGGRAPH, 2018. 2 13

    2018

Pith tools

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