Pith. sign in

REVIEW 3 major objections 7 minor 8 cited by

Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis

T0 review · 3 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Video-to-4D generation is recast as generating a canonical Gaussian splat plus a temporally coherent latent variation field, producing animated 3D content from a single video in about 4.5 seconds.

desk verdict Genuinely new feedforward video-to-4D pipeline, but the headline numbers carry an admitted and unquantified canonical-GS distribution shift that a serious referee should push on. read the letter →

arxiv 2507.23785 v1 pith:ZRNGS3IR submitted 2025-07-31 cs.CV

Bowen Zhang , Sicheng Xu , Chuxin Wang , Jiaolong Yang , Feng Zhao , Dong Chen , Baining Guo This is my paper
classification cs.CV
keywords video-to-4DgenerationGaussianVariationFielddynamicSplattinglatentdiffusionmodelmesh-guidedinterpolation4Dcontenttemporaltransformersingle-videoanimation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that single-video 4D generation becomes tractable when motion is separated from appearance: first synthesize a canonical static 3D Gaussian splat, then generate only the per-frame changes to each Gaussian's attributes, which the paper calls the Gaussian Variation Field. To make that second step affordable, the authors build a Direct 4DMesh-to-GS Variation Field VAE that reads 4D mesh animations in a single forward pass and compresses each frame's motion into a 512-dimensional latent, avoiding the slow per-instance dynamic-Gaussian fitting that earlier pipelines required. A transformer-based diffusion model, conditioned on the input video and the canonical splat, then learns the distribution of these variation-field latents. The payoff, if the claims hold, is that video-to-4D becomes a seconds-scale, single-video process with better measured quality than both optimization-based and feedforward baselines, and one that carries over to in-the-wild videos despite being trained on synthetic data only.

What carries the argument

The load-bearing object is the Gaussian Variation Field $V = \{\Delta G_t\}_{t=1}^{T}$, where each $\Delta G_t = \{\Delta p_t, \Delta s_t, \Delta q_t, \Delta c_t, \Delta \alpha_t\}$ records how every canonical Gaussian changes at frame $t$, so the whole animation is $G_t = G_1 + \Delta G_t$. Motion enters through mesh-guided interpolation: for each canonical Gaussian position, its $K$ nearest neighbors (here $K=8$) in the canonical point cloud give weights via an adaptive radius and decay rate $\beta = 7.0$, interpolating the mesh vertex displacement fields into pseudo-ground-truth Gaussian displacements. Those interpolated displacements serve two roles at once: they become the query vectors of the cross-attention encoder (after farthest-point sampling) and they are the targets of the mesh-guided loss $\mathcal{L}_{\mathrm{mg}} = \sum_{t=1}^{T} \|\Delta p_t - \Delta p_t^{\mathrm{interp}}\|_2^2$. The encoder compresses 8192 point displacements to a $512 \times 16$ latent per frame; the decoder applies stacked self-attention and then a cross-attention layer queried by all canonical Gaussian attributes. The diffusion model is a Diffusion Transformer (a transformer that denoises corrupted latents) augmented with temporal self-attention, conditioned through cross-attention on DINOv2 video features (a self-supervised visual feature extractor) and canonical Gaussian positions, whose positional embeddings keep the model aware of which Gaussian each latent token describes.

What would settle it

On a held-out set of mesh animations with strong non-rigid deformation or topological change, fit per-instance dynamic 4D Gaussian Splatting in the conventional way and compare each Gaussian's fitted displacement against the displacement decoded by the variation-field VAE. If large errors concentrate in Gaussians far from the mesh surface, where nearest-vertex interpolation is uninformative, while rendered images stay sharp because the image-level loss dominates, then the mesh-guided loss is training the latent on distorted motion and the claim of faithful motion compression fails. A second check is to decode latents from VAEs trained with different mesh-guided loss weights and test whether latent-space structure tracks the interpolation error.

Watch

Extended reading notes

Core claim

The paper's central claim is that 4D generation decomposes cleanly into canonical 3D Gaussian Splatting (3DGS), a point-based 3D rendering representation, plus a Gaussian Variation Field: the per-frame deltas in position, scale, rotation, color, and opacity of each Gaussian relative to the canonical splat. The variation-field VAE obtains motion directly from mesh animation data, using K-nearest-neighbor interpolation of mesh vertex displacements to create motion-aware query vectors for a perceiver-style cross-attention encoder and to supervise a mesh-guided loss, alongside image-level rendering losses. This compresses 8192 sampled surface points down to a latent of 512 tokens per frame, and the decoder reconstructs the full dynamic splat sequence from that latent. A Gaussian Variation Field diffusion model, a Diffusion Transformer with temporal self-attention conditioned on video frame features and farthest-sampled canonical Gaussians, learns the latent distribution. The authors report consistent improvement over prior video-to-4D methods on PSNR, LPIPS, SSIM, CLIP, and FVD, generation in about 4.5 seconds on a single A100, and generalization to in-the-wild videos and to animating existing 3D assets despite training exclusively on synthetic animations.

Load-bearing premise

The whole motion pipeline rests on the assumption that a Gaussian's correct movement is well approximated by interpolating the displacements of its nearest mesh vertices, since those interpolated displacements are used both to build the motion-aware queries and as the supervised training target, and this pseudo ground truth is never validated against independently fitted dynamic Gaussians.

Editorial extensions

If this is right

  • Video-to-4D training data can scale with available 3D animation assets rather than reconstruction compute, because the VAE encodes mesh animations directly and no longer requires fitting dynamic Gaussians for every training instance.
  • End-to-end generation from a single video takes about 4.5 seconds on an A100 (3.0 s for canonical splat creation, 1.5 s for variation-field diffusion), similar to the fastest feedforward baseline and orders of magnitude faster than optimization-based methods.
  • The same model can animate an existing 3D asset: the user provides a canonical splat and a motion video, and the variation-field diffusion transfers the motion, which the paper demonstrates on previously unseen objects.
  • Long animations can be produced autoregressively by treating the last frame's Gaussians as the next segment's canonical state, extending generation beyond the 24-frame training window to at least 120 frames.
  • The reported metrics (PSNR 18.47, LPIPS 0.114, SSIM 0.901, CLIP 0.935, FVD 476.83) are the best among the compared methods, indicating both sharper frames and better temporal coherence.

Reading between the lines

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

  • Editorial inference: the mesh-guided interpolation is an untested proxy for true per-Gaussian motion; a direct comparison of VAE-decoded displacements against independently fitted 4D Gaussian Splatting would show whether error clusters in off-surface Gaussians and near topology changes, where K-nearest-neighbor interpolation cannot be faithful.
  • Editorial inference: the canonical-plus-variation decomposition invites operations the paper does not demonstrate, such as transferring one video's motion onto multiple assets, interpolating between two motion latents, or blending temporal segments.
  • Editorial inference: the acknowledged failure mode, where a misaligned canonical splat (wrong head pose, eyes, or lighting relative to the input video) degrades the animation, means the framework's ceiling is currently set by the frozen pretrained static 3D model; an end-to-end version that generates canonical and variation jointly, which the paper proposes as future work, would directly target th
  • Editorial inference: the in-the-wild generalization claim rests on qualitative examples; a quantitative benchmark with real captured objects and multi-view ground truth would measure the actual synthetic-to-real gap.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 7 minor

Summary. This paper presents a framework for video-to-4D generation that decomposes the problem into (i) a Direct 4DMesh-to-GS Variation Field VAE, which encodes a canonical 3D Gaussian Splatting (GS) and per-frame attribute variations derived from 3D mesh animations into a 512-dimensional latent per time step using a cross-attention encoder with mesh-guided KNN-interpolated displacement queries, and (ii) a video- and canonical-GS-conditioned diffusion transformer that denoises this compact latent. At inference, the canonical GS is produced by a pretrained image-to-3D diffusion model (TRELLIS), the variation field is decoded and applied to the canonical GS, yielding a 4D representation in about 4.5 seconds. Experiments on a 100-object test set report improvements over Consistent4D, SC4D, STAG4D, DreamGaussian4D, and L4GM on PSNR, LPIPS, SSIM, CLIP, and FVD, with ablated VAE components and a single diffusion ablation.

Significance. If the results hold, the paper's main contribution is an efficient, single-pass encoding of 4D mesh animation into a compact latent space that avoids per-instance dynamic-GS fitting, enabling a video-conditioned 4D diffusion model at competitive speed (4.5 s). The paper is commendably explicit about its failure mode (Sec. F, Fig. 10) and provides component ablations (Tables 2, 5, 6) for the VAE, including joint decoder finetuning and interpolation hyperparameters. The credibility of the central claim, however, rests on two unquantified points: the effect of replacing ground-truth-derived canonical GS (used in training, Eq. 2) with TRELLIS-generated canonical GS at inference (Sec. 3.3), and the accuracy of the mesh-guided pseudo-displacements (Eqs. 3-4, 9) used as training supervision. Neither is validated against an oracle or independently fitted dynamic Gaussians, and the quantitative comparison contains no error bars and only in-distribution held-out objects.

major comments (3)
  1. [Sec. 3.3, Sec. F, Table 1] The canonical GS used to train the VAE and diffusion model is produced by the frozen EGS/DGS autoencoder from ground-truth meshes (Sec. 3.1, Eq. 2), whereas at inference it comes from the pretrained TRELLIS image-to-3D model with only azimuth alignment (Sec. 3.3, App. A.2). The paper acknowledges this shift in Sec. F and shows one failure (Fig. 10), but it does not report how often across the 100-object test set the static model produces misaligned canonical GS, nor the performance gap between the full pipeline and an oracle that uses ground-truth-derived canonical GS. Because Table 1 compares full pipelines, the claimed superior generation quality could be driven largely by static-model quality rather than the proposed variation-field components; this needs an oracle or controlled perturbation experiment.
  2. [Sec. 3.1, Eqs. 3-4, 9] The mesh-guided loss (Eq. 9) treats the KNN-interpolated Gaussian displacements Δp_interp (Eqs. 3-4) as ground truth for every canonical Gaussian, including Gaussians whose nearest mesh vertices are on the opposite side of a thin part or in regions where topology changes. The ablations in Table 6 vary K and β but provide no independent check (e.g., 4DGS fitting on a subset) of whether the interpolated displacements match the true Gaussian motion. Since the diffusion model is trained on latents produced by this VAE, an incorrect motion latent would be inherited by the generative model, so the soundness of the central representation depends on this validation.
  3. [Sec. 4.1, Table 1] The quantitative evaluation is performed on 100 objects drawn from Objaverse/Objaverse-XL (93 of them from Objaverse-XL, the same distribution used for training), and no error bars or multiple-seed variance are reported. The in-the-wild claims rest on qualitative figures (Figs. 1, 5, 9). To support 'high-fidelity video-to-4D synthesis' for real videos, the authors should either add quantitative metrics on a small set of real-world videos (with an appropriate evaluation protocol, even if imperfect) or explicitly restrict the claim to synthetic-distribution inputs.
minor comments (7)
  1. [Sec. 3.1] The phrase 'faithful motion reconsturction' should be corrected to 'faithful motion reconstruction'.
  2. [Sec. 3.2] The phrase 'fartherest sampled' should be 'farthest sampled'.
  3. [Eq. 3] The adaptive radius r_i is defined as the RMS distance to the K nearest neighbors; the notation r_i^2 in the exponent should be clarified to avoid confusion between the radius and its square.
  4. [Fig. 1] The caption repeats 'In-the-wild Input Video' twice; the second occurrence appears to be a duplicate and should be removed.
  5. [Table 1] The table should state the number of frames used for FVD and whether the reported values come from a single run; reporting standard deviations across seeds would strengthen the comparison.
  6. [Sec. 4.2 and App. D.1] The model is trained on 24-frame sequences and evaluated on 32 frames; the paper should clarify whether the autoregressive extension described in App. D.1 was used for the Table 1 results or whether the model is evaluated with a fixed 32-frame condition.
  7. [Sec. 4.1] The test set description should clarify the provenance of the 7 Consistent4D instances: if they are real videos, the ground-truth 3D models used for PSNR/LPIPS/SSIM computation should be described, since the metrics are computed on renderings.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: variation-field VAE and diffusion are trained on independent mesh-derived and held-out objectives; self-citation to TRELLIS is load-bearing but externally supported.

full rationale

The derivation chain is not circular. For the VAE, the pseudo-ground-truth displacements are computed by KNN interpolation of ground-truth mesh vertex displacements (Eqs. 3-4), and the same point-cloud displacement fields are used as keys/values in the encoder (Eq. 5); the mesh-guided loss (Eq. 9) supervises the decoder's predicted displacements against those interpolated targets. This is an ordinary autoencoding objective with a bottlenecked latent (L=512 vs N=8192): the target is a function of the input mesh signal, not of the network's output, and the decoder must reconstruct all NG Gaussians from the compressed latent, so no identity shortcut makes the loss vanish by construction. The image-level loss (Eq. 8) further ties reconstruction to renderings of the ground-truth animation. For the diffusion model, training uses L_simple (Eq. 10) on latents produced by the VAE from ground-truth 4D meshes and conditioned on video frames, and evaluation is on held-out test videos against fixed metrics (PSNR, LPIPS, SSIM, FVD); no test metric is used as a training objective. The main self-citation is TRELLIS [81], which supplies the frozen EGS, finetuned DGS, and inference-time canonical GS generator, with four overlapping authors. This dependency is load-bearing for the full pipeline, but [81] is an externally evaluated static-3D model, so the citation provides independent evidence rather than a self-referential justification. Section F explicitly acknowledges that misaligned canonical GS can cause suboptimal animations; that is a train/test distribution-shift limitation, not a definitional equivalence between inputs and outputs. Accordingly, no circular step can be exhibited; the score of 2 reflects only the presence of an author-overlapping architectural dependency, not circular reasoning.

Assumptions & free parameters 7 free parameters · 4 assumptions · 0 invented entities

The method depends on several external inputs: the pretrained TRELLIS model for canonical GS, mesh correspondences for motion supervision, DINOv2 features for video conditioning, and the assumption that Objaverse animations cover the target distribution. The free parameters are hyperparameters chosen by hand or by small ablations; none are fitted to the final evaluation metrics.

free parameters (7)
  • beta (interpolation decay) = 7.0
    Controls locality of mesh-guided interpolation weights in Eq. (3); set by hand and ablated in Table 6.
  • K (nearest neighbors) = 8
    Number of mesh vertices used for interpolation; selected by ablation in Table 6.
  • lambda_mg = 1.0
    Weight of mesh-guided loss Eq. (9); hand-set.
  • lambda_lpips, lambda_ssim, lambda_kl = 0.2, 0.2, 1e-6
    Loss weights in Eq. (8) and total loss; hand-set.
  • Latent length L and feature dim C = 512 and 16
    Compression hyperparameters; encode N=8192 points into 512 latents with 16 channels.
  • Aesthetic score threshold = 5.5
    Filtering criterion for Objaverse-XL data, following [81].
  • Training frames T = 24 train, 32 inference
    Sequence length; longer inference via autoregression.
assumptions (4)
  • domain assumption Pretrained TRELLIS autoencoder EGS/DGS faithfully reconstructs canonical 3D assets from meshes and images.
    Used to obtain canonical GS G1 from mesh M1 (Eq. 2) and to generate canonical GS at inference; if G1 is misaligned with the input video, the variation field cannot fix it (acknowledged in Appendix F).
  • domain assumption Mesh vertex correspondences across frames are accurate and KNN interpolation transfers them to Gaussians.
    The mesh-guided loss L_mg (Eq. 9) and encoder queries (Eq. 4) rely on these pseudo-displacements; inaccurate correspondences corrupt training.
  • domain assumption DINOv2 per-frame features are a sufficient condition for motion generation.
    The diffusion model conditions on C_v extracted from DINOv2 (Sec. 3.2); if appearance features do not disambiguate motion, generation quality degrades.
  • domain assumption Objaverse animations are representative of real-world dynamic objects.
    The model is trained only on synthetic Objaverse and Objaverse-XL animations; in-the-wild generalization is shown qualitatively but not guaranteed.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis." pith.science (2026). https://pith.science/paper/ZRNGS3IR

@misc{pith2026250723785,
  author       = {Pith},
  title        = {Pith review of: Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZRNGS3IR}},
  note         = {Machine review of arXiv:2507.23785}
}
read the original abstract

In this paper, we present a novel framework for video-to-4D generation that creates high-quality dynamic 3D content from single video inputs. Direct 4D diffusion modeling is extremely challenging due to costly data construction and the high-dimensional nature of jointly representing 3D shape, appearance, and motion. We address these challenges by introducing a Direct 4DMesh-to-GS Variation Field VAE that directly encodes canonical Gaussian Splats (GS) and their temporal variations from 3D animation data without per-instance fitting, and compresses high-dimensional animations into a compact latent space. Building upon this efficient representation, we train a Gaussian Variation Field diffusion model with temporal-aware Diffusion Transformer conditioned on input videos and canonical GS. Trained on carefully-curated animatable 3D objects from the Objaverse dataset, our model demonstrates superior generation quality compared to existing methods. It also exhibits remarkable generalization to in-the-wild video inputs despite being trained exclusively on synthetic data, paving the way for generating high-quality animated 3D content. Project page: https://gvfdiffusion.github.io/.

Figures

Figures reproduced from arXiv: 2507.23785 by the authors.

Figure 1
Figure 1. Our model is capable of creating high-fidelity 4D objects from in-the-wild video inputs. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Framework of 4DMesh-to-GS Variation Field VAE. Our VAE directly encodes 3D animation data into Gaussian Variation Fields within a compact latent space, optimized through image-level reconstruction loss and the proposed mesh-guided loss. 3. Method Given an input video sequence I = {It} T t=1 of an ob￾ject, our goal is to generate a sequence of 3DGS models G = {Gt} T t=1 that captures both the shape, appearance, and m… view at source ↗
Figure 3
Figure 3. Architecture of Gaussian Variation Field diffusion model. Our model is built upon diffusion transformer, which takes noised latent as input and gradually denoises it conditioned on the video sequence and canonical GS. aware of all canonical Gaussian attributes, we use all pa￾rameters of G1 to query the latent output through a cross attention layer: Qd = fgs(G1) + P E(G1), Kd = Vd = zn, ∆Gt = CrossAttn(Qd, Kd, Vd), (… view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison with previous state-of-the-art methods. Our model directly learns the distribution of Gaussian Variation Fields, enabling high-fidelity 4D generation with coherent temporal dynamics. size N = 8192, latent size L = 512, and feature dimen￾sion C = …
Figure 5
Figure 5. Figure 5: More generation results of our model including in-the-wild videos (left) and videos from test set (right). [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Visual ablation of VAE. motion reproduction. Despite being trained on synthetic data, the model exhibits strong generalization capability by effectively capturing motion patterns from in-the-wild video inputs. Furthermore, the model successfully handles chal￾lenging mu…
Figure 7
Figure 7. Figure 7: Our model is also capable of creating animations for existing 3D assets with conditional videos. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: Sample of our autoregressive generation result. [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: More generation results of real-world input videos. [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: Failure case. When the pretrained static 3D generative model produces canonical GS that are not well-aligned with the conditional video frames, our Gaussian Variation Field diffusion model struggles to bridge this inconsistency, resulting in suboptimal animations. ass…
Figure 11
Figure 11. Figure 11: Additional visual results of VAE reconstruction. [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: More visual comparison with SOTA Methods. [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]
Figure 13
Figure 13. Figure 13: More results of animating existing 3D model input with conditional videos. [PITH_FULL_IMAGE:figures/full_fig_p019_13.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. World Tracing: Generative Pixel-Aligned Geometry Beyond the Visible

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    World Tracing introduces a multi-layer pixel-aligned 3D point representation instantiated via a diffusion transformer (WT-DiT) trained with pixel-space flow matching to jointly reconstruct visible surfaces and generat...

  2. PerpetualWonder: Long-Horizon Action-Conditioned 4D Scene Generation

    cs.CV 2026-02 unverdicted novelty 7.0 of 10

    PerpetualWonder introduces a closed-loop generative simulator with a unified physical-visual representation for long-horizon action-conditioned 4D scene generation from one image.

  3. AniGS: Bridging Rendering and Diffusion Prior for 3D Scene Animation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    AniGS animates a static 3D Gaussian Splatting scene by iteratively distilling video-diffusion motion into a time-conditioned deformation field while keeping static regions fixed.

  4. HAT-4D: Lifting Monocular Video for 4D Multi-Object Interactions via Human-Agent Collaboration

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    HAT-4D presents an agentic VLM-plus-human-in-the-loop pipeline for monocular 4D multi-object interaction reconstruction and releases the MVOIK-4D benchmark.

  5. Helix4D: Complex 4D Mesh Generation

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    Helix4D generates high-quality dynamic 4D meshes from videos by extending Trellis2 with sliding-window cross-frame attention anchored on the first frame and a repurposed 4D temporal encoding.

  6. Fast 4D Mesh Generation by Spatio-Temporal Attention Chains

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    A training-free Spatio-Temporal Attention Chain framework accelerates 4D mesh generation 13x, improves quality, scales to 16x longer videos, and supports downstream tracking and camera estimation.

  7. Velox: Learning Representations of 4D Geometry and Appearance

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    Velox compresses dynamic point clouds into latent tokens that support geometry via 4D surface modeling and appearance via 3D Gaussians, showing strong results on video-to-4D generation, tracking, and image-to-4D cloth...

  8. DevilSight: Augmenting Monocular Human Avatar Reconstruction through a Virtual Perspective

    cs.CV 2025-08 reject novelty 5.0 of 10

    A monocular human avatar reconstruction method generates pseudo back-view videos with a fine-tuned diffusion model and uses them as extra training data for a 3D Gaussian avatar.

Reference graph

Works this paper leans on

100 extracted references · 28 canonical work pages · cited by 8 Pith papers

  1. [1]

    4d-fy: Text-to-4d generation using hybrid score distillation sampling

    Sherwin Bahmani, Ivan Skorokhodov, Victor Rong, Gordon Wetzstein, Leonidas Guibas, Peter Wonka, Sergey Tulyakov, Jeong Joon Park, Andrea Tagliasacchi, and David B Lindell. 4d-fy: Text-to-4d generation using hybrid score distillation sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7996–8006,

  2. [2]

    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. 1

    arXiv 2023

  3. [3]

    Align your latents: High-resolution video synthesis with latent 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 latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22563–22575, 2023. 1, 2

    2023

  4. [4]

    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

    2023

  5. [5]

    Motion2vecsets: 4d latent vector set diffusion for non-rigid shape reconstruction and tracking

    Wei Cao, Chang Luo, Biao Zhang, Matthias Nießner, and Jiapeng Tang. Motion2vecsets: 4d latent vector set diffusion for non-rigid shape reconstruction and tracking. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20496–20506, 2024. 2

    2024

  6. [6]

    Large-vocabulary 3d diffusion model with transformer

    Ziang Cao, Fangzhou Hong, Tong Wu, Liang Pan, and Ziwei Liu. Large-vocabulary 3d diffusion model with transformer. arXiv preprint arXiv:2309.07920, 2023. 2

    arXiv 2023

  7. [7]

    Efficient geometry-aware 3d generative adversarial networks

    Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16123–16133, 2022. 2

    2022

  8. [8]

    Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation

    Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation. ArXiv preprint, abs/2303.13873, 2023. 2

    arXiv 2023

Show all 100 references
  1. [9]

    Primdiffusion: V olumetric primitives diffusion for 3d human generation

    Zhaoxi Chen, Fangzhou Hong, Haiyi Mei, Guangcong Wang, Lei Yang, and Ziwei Liu. Primdiffusion: V olumetric primitives diffusion for 3d human generation. Advances in Neural Information Processing Systems, 36:13664–13677,

  2. [10]

    3dtopia-xl: Scaling high- quality 3d asset generation via primitive diffusion

    Zhaoxi Chen, Jiaxiang Tang, Yuhao Dong, Ziang Cao, Fangzhou Hong, Yushi Lan, Tengfei Wang, Haozhe Xie, Tong Wu, Shunsuke Saito, et al. 3dtopia-xl: Scaling high- quality 3d asset generation via primitive diffusion. arXiv preprint arXiv:2409.12957, 2024. 2

    2024 arXiv

  3. [11]

    Dynamic gaussian splatting from markerless motion capture reconstruct infants movements

    R James Cotton and Colleen Peyton. Dynamic gaussian splatting from markerless motion capture reconstruct infants movements. In Proceedings of the IEEE/CVF Winter Con- ference on Applications of Computer Vision, pages 60–68,

  4. [12]

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

    Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram V oleti, Samir Yitzhak Gadre, et al. Objaverse-xl: A universe of 10m+ 3d objects. Advances in Neural In- formation Processing Systems, 36:35799–35813, 20...

    2023

  5. [13]

    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 Rec...

    2023

  6. [14]

    Gram: Generative radiance manifolds for 3d-aware image generation

    Yu Deng, Jiaolong Yang, Jianfeng Xiang, and Xin Tong. Gram: Generative radiance manifolds for 3d-aware image generation. In IEEE/CVF International Conference on Com- puter Vision, 2022. 2

    2022

  7. [15]

    Diffusion models beat gans on image synthesis

    Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in Neural Informa- tion Processing Systems, 34:8780–8794, 2021. 1

    2021

  8. [16]

    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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12479–12488, 2023. 2

    2023

  9. [17]

    Get3d: A generative model of high quality 3d textured shapes learned from images

    Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja Fidler. Get3d: A generative model of high quality 3d textured shapes learned from images. arXiv preprint arXiv:2209.11163, 2022. 2

    2022 arXiv

  10. [18]

    Factorizing text-to-video generation by explicit image conditioning

    Rohit Girdhar, Mannat Singh, Andrew Brown, Quentin Du- val, Samaneh Azadi, Sai Saketh Rambhatla, Akbar Shah, Xi Yin, Devi Parikh, and Ishan Misra. Factorizing text-to-video generation by explicit image conditioning. In European Conference on Computer Vision, pages 205–224. Springer,

  11. [19]

    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. 1

    2023 arXiv

  12. [20]

    3dgen: Triplane latent diffusion for textured mesh generation

    Anchit Gupta, Wenhan Xiong, Yixin Nie, Ian Jones, and Barlas O˘guz. 3dgen: Triplane latent diffusion for textured mesh generation. arXiv preprint arXiv:2303.05371, 2023. 2

    2023 arXiv

  13. [21]

    Efficient diffu- sion training via min-snr weighting strategy

    Tiankai Hang, Shuyang Gu, Chen Li, Jianmin Bao, Dong Chen, Han Hu, Xin Geng, and Baining Guo. Efficient diffu- sion training via min-snr weighting strategy. arXiv preprint arXiv:2303.09556, 2023. 1

    2023 arXiv

  14. [22]

    Gvgen: Text-to-3d generation with volumetric representation

    Xianglong He, Junyi Chen, Sida Peng, Di Huang, Yang- guang Li, Xiaoshui Huang, Chun Yuan, Wanli Ouyang, and Tong He. Gvgen: Text-to-3d generation with volumetric representation. arXiv preprint arXiv:2403.12957, 2024. 2 9

    2024 arXiv

  15. [23]

    Imagen video: High definition video generation with diffusion mod- els

    Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al. Imagen video: High definition video generation with diffusion mod- els. arXiv preprint arXiv:2210.02303, 2022. 1

    arXiv

  16. [24]

    Lrm: Large reconstruction model for single image to 3d

    Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:2311.04400, 2023. 1

    2023 arXiv

  17. [25]

    Sc-gs: Sparse-controlled gaussian splatting for editable dynamic scenes

    Yi-Hua Huang, Yang-Tian Sun, Ziyi Yang, Xiaoyang Lyu, Yan-Pei Cao, and Xiaojuan Qi. Sc-gs: Sparse-controlled gaussian splatting for editable dynamic scenes. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4220–4230, 2024. 2

    2024

  18. [26]

    Perceiver io: A general architecture for structured inputs & outputs

    Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Kop- pula, Daniel Zoran, Andrew Brock, Evan Shelhamer, et al. Perceiver io: A general architecture for structured inputs & outputs. arXiv preprint arXiv:2107.14795, 2021. 2

    2021 arXiv

  19. [27]

    Perceiver: General perception with iterative attention

    Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira. Perceiver: General perception with iterative attention. In International confer- ence on machine learning, pages 4651–4664. PMLR, 2021. 2

    2021

  20. [28]

    Consistent4d: Consistent 360 {\deg} dynamic ob- ject generation from monocular video

    Yanqin Jiang, Li Zhang, Jin Gao, Weimin Hu, and Yao Yao. Consistent4d: Consistent 360 {\deg} dynamic ob- ject generation from monocular video. arXiv preprint arXiv:2311.02848, 2023. 2, 5, 6

    2023 arXiv

  21. [29]

    Shap-e: Generat- ing conditional 3d implicit functions

    Heewoo Jun and Alex Nichol. Shap-e: Generat- ing conditional 3d implicit functions. arXiv preprint arXiv:2305.02463, 2023. 2

    2023 arXiv

  22. [30]

    A style-based generator architecture for generative adversarial networks

    Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4401–4410, 2019. 1

    2019

  23. [31]

    Analyzing and improv- ing the image quality of stylegan

    Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improv- ing the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recog- nition, pages 8110–8119, 2020. 1

    2020

  24. [32]

    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, 42(4), 2023. 2, 3

    2023

  25. [33]

    Hunyuanvideo: A systematic framework for large video generative models, 2025

    Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jian- wei Zhang, Kathrina Wu, Qin Lin, Junkun Yuan, Yanxin Long, Aladdin Wang, Andong Wang, Changlin Li, Duojun Huang, Fang Yang, Hao Tan, Hongmei Wang, Jacob Song, Jiawang Bai,...

    2025

  26. [34]

    Kuaishou. Kling. https://klingai.kuaishou.com, 2024. 8, 14, 15

    2024

  27. [35]

    Ln3diff: Scalable latent neural fields diffusion for speedy 3d generation

    Yushi Lan, Fangzhou Hong, Shuai Yang, Shangchen Zhou, Xuyi Meng, Bo Dai, Xingang Pan, and Chen Change Loy. Ln3diff: Scalable latent neural fields diffusion for speedy 3d generation. In ECCV, 2024. 2

    2024

  28. [36]

    Gaussianbody: Clothed human re- construction via 3d gaussian splatting

    Mengtian Li, Shengxiang Yao, Zhifeng Xie, Keyu Chen, and Yu-Gang Jiang. Gaussianbody: Clothed human re- construction via 3d gaussian splatting. arXiv preprint arXiv:2401.09720, 2024. 2

    2024 arXiv

  29. [37]

    Craftsman: High-fidelity mesh generation with 3d native generation and interactive geometry refiner

    Weiyu Li, Jiarui Liu, Rui Chen, Yixun Liang, Xuelin Chen, Ping Tan, and Xiaoxiao Long. Craftsman: High-fidelity mesh generation with 3d native generation and interactive geometry refiner. arXiv preprint arXiv:2405.14979, 2024. 2

    2024 arXiv

  30. [38]

    Diffusion4d: Fast spatial-temporal consis- tent 4d generation via video diffusion models.arXiv preprint arXiv:2405.16645, 2024

    Hanwen Liang, Yuyang Yin, Dejia Xu, Hanxue Liang, Zhangyang Wang, Konstantinos N Plataniotis, Yao Zhao, and Yunchao Wei. Diffusion4d: Fast spatial-temporal consis- tent 4d generation via video diffusion models.arXiv preprint arXiv:2405.16645, 2024. 2, 15

    2024 arXiv

  31. [39]

    Luciddreamer: Towards high- fidelity text-to-3d generation via interval score matching

    Yixun Liang, Xin Yang, Jiantao Lin, Haodong Li, Xiaogang Xu, and Yingcong Chen. Luciddreamer: Towards high- fidelity text-to-3d generation via interval score matching. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6517–6526, 2024. 2

    2024

  32. [40]

    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. 2

    2023

  33. [41]

    Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps

    Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. Advances in Neural Information Processing Systems, 35:5775–5787,

  34. [42]

    Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis

    Jonathon Luiten, Georgios Kopanas, Bastian Leibe, and Deva Ramanan. Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis. arXiv preprint arXiv:2308.09713, 2023. 2

    2023 arXiv

  35. [43]

    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. 2

    2021

  36. [44]

    Diffrf: Rendering-guided 3d radiance field diffusion

    Norman M ¨uller, Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bulo, Peter Kontschieder, and Matthias Nießner. Diffrf: Rendering-guided 3d radiance field diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4328–4338, 2023. 2

    2023

  37. [45]

    Improved denoising diffusion probabilistic models

    Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In International Conference on Machine Learning, pages 8162–8171. PMLR,

  38. [46]

    Autodecoding latent 3d diffusion models

    Evangelos Ntavelis, Aliaksandr Siarohin, Kyle Olszewski, Chaoyang Wang, Luc Van Gool, and Sergey Tulyakov. Autodecoding latent 3d diffusion models. arXiv preprint arXiv:2307.05445, 2023. 2 10

    2023 arXiv

  39. [47]

    Video generation models as world simula- tors

    OpenAI. Video generation models as world simula- tors. https://openai.com/index/video-generation-models-as- world-simulators, 2024. 8, 15

    2024

  40. [48]

    Dinov2: Learning robust visual features without supervision

    Maxime Oquab, Timoth´ee Darcet, Th´eo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 2, 5, 14

    2023 arXiv

  41. [49]

    Effi- cient4d: Fast dynamic 3d object generation from a single- view video

    Zijie Pan, Zeyu Yang, Xiatian Zhu, and Li Zhang. Effi- cient4d: Fast dynamic 3d object generation from a single- view video. arXiv preprint arXiv:2401.08742, 2024. 2

    2024

  42. [50]

    Nerfies: Deformable neural radiance fields

    Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5865–5874, 2021. 2

    2021

  43. [51]

    Hypernerf: A higher- dimensional representation for topologically varying neural radiance fields

    Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin- Brualla, and Steven M Seitz. Hypernerf: A higher- dimensional representation for topologically varying neural radiance fields. arXiv preprint arXiv:2106.13228, 2021. 2

    2021 arXiv

  44. [52]

    Scalable diffusion models with transformers

    William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pages 4195–4205,

  45. [53]

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

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

    2022 arXiv

  46. [54]

    D-nerf: Neural radiance fields for dynamic scenes

    Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. D-nerf: Neural radiance fields for dynamic scenes. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 10318–10327, 2021. 2

    2021

  47. [55]

    Hierarchical text-conditional image genera- tion with clip latents

    Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image genera- tion with clip latents. arXiv preprint arXiv:2204.06125, 1 (2):3, 2022. 1

    2022 arXiv

  48. [56]

    Dreamgaussian4d: Generative 4d gaussian splatting

    Jiawei Ren, Liang Pan, Jiaxiang Tang, Chi Zhang, Ang Cao, Gang Zeng, and Ziwei Liu. Dreamgaussian4d: Generative 4d gaussian splatting. arXiv preprint arXiv:2312.17142 ,

    arXiv

  49. [57]

    L4gm: Large 4d gaussian reconstruction model

    Jiawei Ren, Cheng Xie, Ashkan Mirzaei, Karsten Kreis, Ziwei Liu, Antonio Torralba, Sanja Fidler, Seung Wook Kim, Huan Ling, et al. L4gm: Large 4d gaussian reconstruction model. Advances in Neural Information Processing Systems, 37:56828–56858, 2025. 2, 5, 6, 14

    2025

  50. [58]

    Xcube: Large-scale 3d generative modeling using sparse voxel hierarchies

    Xuanchi Ren, Jiahui Huang, Xiaohui Zeng, Ken Museth, Sanja Fidler, and Francis Williams. Xcube: Large-scale 3d generative modeling using sparse voxel hierarchies. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4209–4219, 2024. 2

    2024

  51. [59]

    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. 1, 2

    2022

  52. [60]

    Zero123++: a single image to consistent multi-view diffusion 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 diffusion base model. arXiv preprint arXiv:2310.15110 ,

    arXiv

  53. [61]

    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. 2, 6

    2023 arXiv

  54. [62]

    3d neural field generation using triplane diffusion

    J Ryan Shue, Eric Ryan Chan, Ryan Po, Zachary Ankner, Jiajun Wu, and Gordon Wetzstein. 3d neural field generation using triplane diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20875–20886, 2023. 2

    2023

  55. [63]

    Text-to-4d dy- namic scene generation

    Uriel Singer, Shelly Sheynin, Adam Polyak, Oron Ashual, Iurii Makarov, Filippos Kokkinos, Naman Goyal, Andrea Vedaldi, Devi Parikh, Justin Johnson, et al. Text-to-4d dy- namic scene generation. arXiv preprint arXiv:2301.11280,

    arXiv

  56. [64]

    3d generation on imagenet

    Ivan Skorokhodov, Aliaksandr Siarohin, Yinghao Xu, Jian Ren, Hsin-Ying Lee, Peter Wonka, and Sergey Tulyakov. 3d generation on imagenet. arXiv preprint arXiv:2303.01416,

    arXiv

  57. [65]

    Dreamcraft3d: Hierarchi- cal 3d generation with bootstrapped diffusion prior

    Jingxiang Sun, Bo Zhang, Ruizhi Shao, Lizhen Wang, Wen Liu, Zhenda Xie, and Yebin Liu. Dreamcraft3d: Hierarchi- cal 3d generation with bootstrapped diffusion prior. arXiv preprint arXiv:2310.16818, 2023. 2

    2023 arXiv

  58. [66]

    Dreamgaussian: Generative gaussian splatting for effi- cient 3d content creation

    Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for effi- cient 3d content creation. arXiv preprint arXiv:2309.16653,

    arXiv

  59. [67]

    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. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 22819–22829, 2023. 2

    2023

  60. [68]

    Lgm: Large multi-view gaussian model for high-resolution 3d content creation.arXiv preprint arXiv:2402.05054, 2024

    Jiaxiang Tang, Zhaoxi Chen, Xiaokang Chen, Tengfei Wang, Gang Zeng, and Ziwei Liu. Lgm: Large multi-view gaussian model for high-resolution 3d content creation.arXiv preprint arXiv:2402.05054, 2024. 1

    2024 arXiv

  61. [69]

    V olumediffu- sion: Flexible text-to-3d generation with efficient volumetric encoder

    Zhicong Tang, Shuyang Gu, Chunyu Wang, Ting Zhang, Jianmin Bao, Dong Chen, and Baining Guo. V olumediffu- sion: Flexible text-to-3d generation with efficient volumetric encoder. arXiv preprint arXiv:2312.11459, 2023. 2

    2023 arXiv

  62. [70]

    Fvd: A new metric for video generation

    Thomas Unterthiner, Sjoerd Van Steenkiste, Karol Kurach, Rapha¨el Marinier, Marcin Michalski, and Sylvain Gelly. Fvd: A new metric for video generation. 2019. 5

    2019

  63. [71]

    Lion: Latent point diffu- sion models for 3d shape generation

    Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, Karsten Kreis, et al. Lion: Latent point diffu- sion models for 3d shape generation. Advances in Neural Information Processing Systems, 35:10021–10039, 2022. 2

    2022

  64. [72]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszko- reit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in Neural Information Processing Systems, 30, 2017. 2, 14

    2017

  65. [73]

    Imagedream: Image-prompt multi-view diffusion for 3d generation

    Peng Wang and Yichun Shi. Imagedream: Image-prompt multi-view diffusion for 3d generation. arXiv preprint arXiv:2312.02201, 2023. 2

    2023 arXiv

  66. [74]

    Rodin: A generative model for sculpting 3d digital avatars using diffusion

    Tengfei Wang, Bo Zhang, Ting Zhang, Shuyang Gu, Jianmin Bao, Tadas Baltrusaitis, Jingjing Shen, Dong Chen, Fang 11 Wen, Qifeng Chen, et al. Rodin: A generative model for sculpting 3d digital avatars using diffusion. In Proceed- ings of the IEEE/CVF Conference on Computer Visio...

    2023

  67. [75]

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

    Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongx- uan Li, Hang Su, and Jun Zhu. Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score dis- tillation. ArXiv preprint, abs/2305.16213, 2023. 2

    2023 arXiv

  68. [76]

    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 Xinggang Wang. 4d gaussian splatting for real-time dynamic scene rendering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 20310–20320,...

    2024

  69. [77]

    Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling

    Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. Advances in Neural Information Processing Systems , 29,

  70. [78]

    Direct3d: Scalable image-to-3d generation via 3d latent diffusion transformer

    Shuang Wu, Youtian Lin, Feihu Zhang, Yifei Zeng, Jingxi Xu, Philip Torr, Xun Cao, and Yao Yao. Direct3d: Scalable image-to-3d generation via 3d latent diffusion transformer. arXiv preprint arXiv:2405.14832, 2024. 2

    2024 arXiv

  71. [79]

    Sc4d: Sparse-controlled video-to-4d generation and motion transfer

    Zijie Wu, Chaohui Yu, Yanqin Jiang, Chenjie Cao, Fan Wang, and Xiang Bai. Sc4d: Sparse-controlled video-to-4d generation and motion transfer. In European Conference on Computer Vision, pages 361–379. Springer, 2024. 5, 6

    2024

  72. [80]

    Gram-hd: 3d-consistent image generation at high resolu- tion with generative radiance manifolds

    Jianfeng Xiang, Jiaolong Yang, Yu Deng, and Xin Tong. Gram-hd: 3d-consistent image generation at high resolu- tion with generative radiance manifolds. arXiv preprint arXiv:2206.07255, 2022. 2

    2022 arXiv

  73. [81]

    Structured 3d latents for scalable and versatile 3d generation

    Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation. arXiv preprint arXiv:2412.01506, 2024. 1, 2, 3, 5, 14, 15, 16

    2024 arXiv

  74. [82]

    Sv4d: Dynamic 3d content generation with multi-frame and multi-view consistency

    Yiming Xie, Chun-Han Yao, Vikram V oleti, Huaizu Jiang, and Varun Jampani. Sv4d: Dynamic 3d content generation with multi-frame and multi-view consistency. arXiv preprint arXiv:2407.17470, 2024. 2

    2024 arXiv

  75. [83]

    Octfusion: Octree- based diffusion models for 3d shape generation

    Bojun Xiong, Si-Tong Wei, Xin-Yang Zheng, Yan-Pei Cao, Zhouhui Lian, and Peng-Shuai Wang. Octfusion: Octree- based diffusion models for 3d shape generation. arXiv preprint arXiv:2408.14732, 2024. 2

    2024 arXiv

  76. [84]

    Atlas gaussians diffusion for 3d generation with infinite number of points

    Haitao Yang, Yuan Dong, Hanwen Jiang, Dejia Xu, Georgios Pavlakos, and Qixing Huang. Atlas gaussians diffusion for 3d generation with infinite number of points. arXiv preprint arXiv:2408.13055, 2024. 2

    2024 arXiv

  77. [85]

    Diffusion 2: Dynamic 3d content generation via score composition of video and multi-view diffusion models

    Zeyu Yang, Zijie Pan, Chun Gu, and Li Zhang. Diffusion 2: Dynamic 3d content generation via score composition of video and multi-view diffusion models. arXiv preprint arXiv:2404.02148, 2024. 2

    2024 arXiv

  78. [86]

    Mosaic-sdf for 3d generative models

    Lior Yariv, Omri Puny, Oran Gafni, and Yaron Lipman. Mosaic-sdf for 3d generative models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4630–4639, 2024. 2

    2024

  79. [87]

    4dgen: Grounded 4d content gener- ation with spatial-temporal consistency

    Yuyang Yin, Dejia Xu, Zhangyang Wang, Yao Zhao, and Yunchao Wei. 4dgen: Grounded 4d content gener- ation with spatial-temporal consistency. arXiv preprint arXiv:2312.17225, 2023. 2

    2023 arXiv

  80. [88]

    Stag4d: Spatial-temporal anchored generative 4d gaussians

    Yifei Zeng, Yanqin Jiang, Siyu Zhu, Yuanxun Lu, Youtian Lin, Hao Zhu, Weiming Hu, Xun Cao, and Yao Yao. Stag4d: Spatial-temporal anchored generative 4d gaussians. In Eu- ropean Conference on Computer Vision , pages 163–179. Springer, 2024. 2, 5, 6

    2024

  81. [89]

    Root mean square layer normalization

    Biao Zhang and Rico Sennrich. Root mean square layer normalization. Advances in Neural Information Processing Systems, 32, 2019. 14

    2019

  82. [90]

    Styleswin: Transformer-based gan for high-resolution image generation

    Bowen Zhang, Shuyang Gu, Bo Zhang, Jianmin Bao, Dong Chen, Fang Wen, Yong Wang, and Baining Guo. Styleswin: Transformer-based gan for high-resolution image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11304–11314, 2022. 1

    2022

  83. [91]

    3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models

    Biao Zhang, Jiapeng Tang, Matthias Niessner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models. ACM Trans- actions on Graphics (TOG), 42(4):1–16, 2023. 2, 3

    2023

  84. [92]

    Rodinhd: High-fidelity 3d avatar generation with diffusion models

    Bowen Zhang, Yiji Cheng, Chunyu Wang, Ting Zhang, Jiao- long Yang, Yansong Tang, Feng Zhao, Dong Chen, and Baining Guo. Rodinhd: High-fidelity 3d avatar generation with diffusion models. arXiv preprint arXiv:2407.06938,

    arXiv

  85. [93]

    Gaussiancube: A structured and explicit radiance rep- resentation for 3d generative modeling

    Bowen Zhang, Yiji Cheng, Jiaolong Yang, Chunyu Wang, Feng Zhao, Yansong Tang, Dong Chen, and Baining Guo. Gaussiancube: A structured and explicit radiance rep- resentation for 3d generative modeling. arXiv preprint arXiv:2403.19655, 2024. 1, 2

    2024 arXiv

  86. [94]

    4diffusion: Multi-view video diffusion model for 4d generation

    Haiyu Zhang, Xinyuan Chen, Yaohui Wang, Xihui Liu, Yun- hong Wang, and Yu Qiao. 4diffusion: Multi-view video diffusion model for 4d generation. Advances in Neural In- formation Processing Systems, 37:15272–15295, 2025. 2

    2025

  87. [95]

    Clay: A controllable large-scale generative model for creat- ing high-quality 3d assets

    Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. Clay: A controllable large-scale generative model for creat- ing high-quality 3d assets. ACM Transactions on Graphics (TOG), 43(4):1–20, 2024. 1, 2

    2024

  88. [96]

    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/CVF Conference on Computer Vision and Pattern Recognition, pages 586–595, 2018. 4, 5

    2018

  89. [97]

    Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation

    Zibo Zhao, Wen Liu, Xin Chen, Xianfang Zeng, Rui Wang, Pei Cheng, Bin Fu, Tao Chen, Gang Yu, and Shenghua Gao. Michelangelo: Conditional 3d shape generation based on shape-image-text aligned latent representation. Advances in Neural Information Processing Systems, 36, 2024. 2

    2024

  90. [98]

    Sdf-stylegan: Implicit sdf-based stylegan for 3d shape gen- eration

    Xinyang Zheng, Yang Liu, Pengshuai Wang, and Xin Tong. Sdf-stylegan: Implicit sdf-based stylegan for 3d shape gen- eration. In Computer Graphics Forum, pages 52–63, 2022. 2

    2022

  91. [99]

    Locally attentional 12 sdf diffusion for controllable 3d shape generation

    Xin-Yang Zheng, Hao Pan, Peng-Shuai Wang, Xin Tong, Yang Liu, and Heung-Yeung Shum. Locally attentional 12 sdf diffusion for controllable 3d shape generation. ACM Transactions on Graphics (ToG), 42(4):1–13, 2023. 2

    2023

  92. [100]

    Shifted Window

    Jun-Yan Zhu, Zhoutong Zhang, Chengkai Zhang, Jiajun Wu, Antonio Torralba, Josh Tenenbaum, and Bill Freeman. Vi- sual object networks: Image generation with disentangled 3d representations. Advances in neural information processing systems, 31, 2018. 2 13 A. Additional Implemen...

    2018

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.