pith. sign in

arxiv: 2605.26109 · v1 · pith:ZX2J4662new · submitted 2026-05-25 · 💻 cs.CV

Helix4D: Complex 4D Mesh Generation

Jiraphon Yenphraphai , Jianqi Chen , Jian Wang , Gordon Qian , Sergey Tulyakov , Rameen Abdal , Raymond A. Yeh , Peter Wonka
show 1 more author
Chaoyang Wang
This is my paper

Pith reviewed 2026-06-29 22:15 UTC · model grok-4.3

classification 💻 cs.CV
keywords 4D mesh generationvideo-conditioned 4Ddynamic meshcross-frame attentiontemporal positional encodingtopology changetransparent materials
0
0 comments X

The pith

Helix4D generates dynamic 4D meshes from video by anchoring cross-frame attention on the first frame and repurposing spatial position bands for time.

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

The paper establishes a method to adapt an image-to-3D mesh generator for video input so that it handles topology changes, transparent materials, thin structures, and inner surfaces without losing quality on those cases. It solves information sharing across frames by adding a sliding-window attention mechanism that draws from the initial frame produced by the base model. It solves the injection of time by modifying the existing 3D position encoding to reuse low-frequency bands for temporal dimensions, keeping the change parameter-free. A reader would care because this produces animated meshes that maintain detail and consistency where prior video-to-4D approaches degrade.

Core claim

Helix4D enables high-quality dynamic mesh generation for complex topology changes, transparent materials, thin structures, and inner surfaces by using sliding-window cross-frame attention anchored on the first frame generated by the base model and a 4D temporal encoding that repurposes redundant low-frequency spatial RoPE bands.

What carries the argument

Sliding-window cross-frame attention anchored on the first generated frame, paired with repurposing of low-frequency spatial RoPE bands into a 4D temporal encoding.

If this is right

  • The base model's performance on rare cases such as transparent objects transfers to the full video sequence via the anchored attention.
  • Temporal information is added to the positional encoding with zero additional learned parameters.
  • The resulting meshes exhibit coherent dynamics across frames on both standard action benchmarks and custom sets with complex changes.
  • Inner surfaces and thin structures remain visible and consistent through the temporal extension.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same anchoring and band-reuse pattern could be tested on other pretrained 3D mesh generators to check whether the quality transfer generalizes.
  • The frequency-band reuse technique might extend to other sequential tasks where a pretrained spatial encoder must gain a time dimension.
  • Longer video sequences would provide a direct test of whether the fixed sliding window maintains coherence without drift.

Load-bearing premise

The sliding-window cross-frame attention anchored on the first frame will transfer the base model's quality on rare cases into the video-conditioned setting without new artifacts, and the repurposed RoPE bands will add temporal information without breaking the pretrained spatial capabilities.

What would settle it

Meshes generated on videos containing transparent objects or inner surfaces show increased artifacts or lost detail relative to single-frame output from the base model alone.

Figures

Figures reproduced from arXiv: 2605.26109 by Chaoyang Wang, Gordon Qian, Jianqi Chen, Jian Wang, Jiraphon Yenphraphai, Peter Wonka, Rameen Abdal, Raymond A. Yeh, Sergey Tulyakov.

Figure 1
Figure 1. Figure 1: We propose Helix4D, a framework that can generate dynamic 3D shapes, including their [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Helix4D pipeline. Given an input video, Helix4D generates a 4D asset through three flow-matching stages: sparse-structure generation, geometry generation, and material generation. Each self-attention layer in the transformer block uses (a) sliding-window cross-frame attention with a first-frame anchor and (b) repurposes low-frequency spatial RoPE bands for temporal encoding. followed by 4D reconstruction [… view at source ↗
Figure 3
Figure 3. Figure 3: Cross-frame attention patterns. Orange cells indicate allowed attention, gray cells indicate masked attention, and red cells indicate attention to the first-frame anchor. Full attention allows every frame to attend to each other, but it is computationally expensive. Causal attention restricts each frame to previous frames, while sliding-window attention limits attention to nearby frames for efficiency. Spa… view at source ↗
Figure 4
Figure 4. Figure 4: Effect of different RoPE ratios. We test how many low-frequency spatial RoPE bands can be removed from a pretrained Trellis2 model by replacing them with identity matrices at inference time (Left). Too small RoPE ratios degrade geometry. In contrast, the results (Right) at ratios 0.4 and 1.0 are visually indistinguishable, suggesting that the removed low-frequency bands contribute little to the output and … view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparison on Helix4DBench. The left column shows input video frames, and each method is shown with geometry/normal renderings and, when available, shading. Compared with ActionMesh [21], Motion 3-to-4 [2], and Mesh4D [7], our method better preserves fine geometry, topology changes, emerging structures, and transparent objects [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparisons of the 4D rotary embedding. Compared with our full model, removing 4D rotary leads to degraded geometry, including rough back surfaces and extra arms. 5.2 Ablation We analyze each design choice by removing one component at a time from the second stage (mesh) model. Results on our held-out TexVerse-1k benchmark are reported in Tab. 3. We observe that all three components contribute t… view at source ↗
read the original abstract

Current video-to-4D methods struggle with complex topology changes, transparent materials, thin structures, and inner surfaces. We present Helix4D, a dynamic mesh generation framework by inheriting the expressive representation of Trellis2, adapting it from image-to-3D to video-conditioned 4D generation. Our design arises from two key questions: (a) how to enable Trellis2's frame-local attention to share information across frames while preserving its pretrained quality on rare cases such as transparent objects and inner surfaces, and (b) how to inject temporal information into a purely 3D positional encoding without breaking pretrained capabilities. We address (a) with a sliding-window cross-frame attention and anchor on the first frame. The first frame is generated by the base Trellis2 model and injected into our model, letting it inherit Trellis2's quality in rare cases through cross-frame attention. We address (b) with a 4D temporal encoding that repurposes redundant low-frequency spatial RoPE bands for time, extending the encoding from 3D with no additional parameters. Extensive experiments show the effectiveness of Helix4D for high-quality dynamic mesh generation on ActionBench and our own challenging complex dynamics set.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents Helix4D, a video-conditioned 4D dynamic mesh generation framework that adapts the Trellis2 image-to-3D model. It introduces a sliding-window cross-frame attention mechanism anchored on the first frame (generated unmodified by Trellis2) to share information across frames while preserving quality on rare cases such as transparent objects and inner surfaces, together with a parameter-free 4D temporal encoding that repurposes redundant low-frequency spatial RoPE bands for time. The authors claim this enables high-quality results on complex topology changes, transparent materials, thin structures, and inner surfaces, supported by experiments on ActionBench and a custom complex-dynamics dataset.

Significance. If the central claims hold, the work would be significant for extending pretrained 3D generative models to the 4D setting without introducing new parameters or retraining, while addressing persistent failure modes (topology change, transparency, inner surfaces) that current video-to-4D methods exhibit. The explicit reuse of an external pretrained model and the parameter-free temporal encoding are concrete strengths that could be directly adopted if the anchoring mechanism proves robust.

major comments (2)
  1. [method description of sliding-window cross-frame attention] The central construction (abstract and method description of sliding-window cross-frame attention) anchors all subsequent frames on the first mesh produced by unmodified Trellis2. For the claim of successful transfer under complex topology changes to hold, this anchor must remain informative even when later frames exhibit different topology or visibility; the manuscript supplies no ablation that removes or perturbs the anchor (while retaining video conditioning) to test whether cross-frame attention can recover from a topologically mismatched first frame.
  2. [Abstract and experimental section] The abstract states that 'extensive experiments show the effectiveness' yet supplies no quantitative metrics, error analysis, ablation tables, or comparison numbers. Without these data it is impossible to assess whether the proposed components actually improve over the Trellis2 baseline or over existing video-to-4D baselines on the claimed failure cases.
minor comments (2)
  1. [Experimental setup] The custom 'challenging complex dynamics set' is mentioned but its construction, size, and selection criteria are not described, preventing reproducibility.
  2. [4D temporal encoding description] Notation for the repurposed RoPE bands (which frequencies are reassigned to time, how the 4D positional encoding is formed) should be made explicit with an equation or pseudocode.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their insightful comments on the anchoring mechanism and the lack of quantitative evaluation. We address each major comment below and commit to revisions where the manuscript can be strengthened.

read point-by-point responses

  1. Referee: The central construction anchors all subsequent frames on the first mesh produced by unmodified Trellis2. For the claim of successful transfer under complex topology changes to hold, this anchor must remain informative even when later frames exhibit different topology or visibility; the manuscript supplies no ablation that removes or perturbs the anchor (while retaining video conditioning) to test whether cross-frame attention can recover from a topologically mismatched first frame.

    Authors: We agree that an ablation perturbing or removing the anchor would provide stronger evidence for the robustness of the cross-frame attention under topology changes. The current design intentionally uses the unmodified Trellis2 output for the first frame to preserve quality on rare cases. In the revised manuscript, we will include an ablation study that replaces the first frame with a perturbed version or a frame from a different model while keeping video conditioning, to demonstrate recovery capability. revision: yes

  2. Referee: The abstract states that 'extensive experiments show the effectiveness' yet supplies no quantitative metrics, error analysis, ablation tables, or comparison numbers. Without these data it is impossible to assess whether the proposed components actually improve over the Trellis2 baseline or over existing video-to-4D baselines on the claimed failure cases.

    Authors: The manuscript currently emphasizes qualitative results on challenging cases like topology changes and transparency, as these are the key failure modes addressed. However, we acknowledge the value of quantitative metrics for objective assessment. We will add quantitative comparisons against Trellis2 baseline and other video-to-4D methods on ActionBench, including metrics such as Chamfer distance for geometry and temporal consistency scores, along with ablation tables for the proposed components. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation builds on external Trellis2 and introduces independent components

full rationale

The paper's central construction explicitly anchors on the unmodified external Trellis2 model to generate the first frame and then adds two new mechanisms (sliding-window cross-frame attention and repurposed RoPE bands for 4D encoding) whose definitions and claimed benefits are stated independently of the target outputs. No equation or claim reduces a fitted parameter to a renamed prediction, no load-bearing premise rests solely on self-citation, and the adaptation from image-to-3D to video-4D is presented as an engineering extension rather than a tautological redefinition. The derivation chain therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities; all details are deferred to the full paper.

pith-pipeline@v0.9.1-grok · 5777 in / 1130 out tokens · 29542 ms · 2026-06-29T22:15:31.792260+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

45 extracted references · 21 canonical work pages · 9 internal anchors

  1. [1]

    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. InProc. CVPR, 2024. 2

    2024

  2. [2]

    Motion 3-to-4: 3D motion reconstruction for 4D synthesis.arXiv preprint arXiv:2601.14253, 2026

    Hongyuan Chen, Xingyu Chen, Youjia Zhang, Zexiang Xu, and Anpei Chen. Motion 3-to-4: 3D motion reconstruction for 4D synthesis.arXiv preprint arXiv:2601.14253, 2026. 2, 3, 4, 6, 7, 8, 14, 18

    work page arXiv 2026

  3. [3]

    V2M4: 4D mesh animation reconstruction from a single monocular video

    Jianqi Chen, Biao Zhang, Xiangjun Tang, and Peter Wonka. V2M4: 4D mesh animation reconstruction from a single monocular video. InProc. ICCV, 2025. 3

    2025

  4. [4]

    DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data

    Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. DreamSim: Learning new dimensions of human visual similarity using synthetic data.arXiv preprint arXiv:2306.09344, 2023. 8

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  5. [5]

    Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material

    Team Hunyuan3D, Shuhui Yang, Mingxin Yang, Yifei Feng, Xin Huang, Sheng Zhang, Zebin He, Di Luo, Haolin Liu, Yunfei Zhao, et al. Hunyuan3D 2.1: From images to high-fidelity 3D assets with production-ready PBR material.arXiv preprint arXiv:2506.15442, 2025. 3

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  6. [6]

    Consistent4D: Consistent 360° dynamic object generation from monocular video.arXiv preprint arXiv:2311.02848, 2023

    Yanqin Jiang, Li Zhang, Jin Gao, Weimin Hu, and Yao Yao. Consistent4D: Consistent 360° dynamic object generation from monocular video.arXiv preprint arXiv:2311.02848, 2023. 2

    work page arXiv 2023

  7. [7]

    Mesh4D: 4D mesh recon- struction and tracking from monocular video.arXiv preprint arXiv:2601.05251, 2026

    Zeren Jiang, Chuanxia Zheng, Iro Laina, Diane Larlus, and Andrea Vedaldi. Mesh4D: 4D mesh recon- struction and tracking from monocular video.arXiv preprint arXiv:2601.05251, 2026. 2, 3, 4, 6, 7, 8, 14, 18

    work page arXiv 2026

  8. [8]

    Diffuman4D: 4D consistent human view synthesis from sparse-view videos with spatio-temporal diffusion models

    Yudong Jin, Sida Peng, Xuan Wang, Tao Xie, Zhen Xu, Yifan Yang, Yujun Shen, Hujun Bao, and Xiaowei Zhou. Diffuman4D: 4D consistent human view synthesis from sparse-view videos with spatio-temporal diffusion models. InProc. ICCV, 2025. 2, 3

    2025

  9. [9]

    Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details

    Zeqiang Lai, Yunfei Zhao, Haolin Liu, Zibo Zhao, Qingxiang Lin, Huiwen Shi, Xianghui Yang, Mingxin Yang, Shuhui Yang, Yifei Feng, et al. Hunyuan3D 2.5: Towards high-fidelity 3D assets generation with ultimate details.arXiv preprint arXiv:2506.16504, 2025. 3

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  10. [10]

    ReRoPE: Repurposing RoPE for relative camera control.arXiv preprint arXiv:2602.08068, 2026

    Chunyang Li, Yuanbo Yang, Jiahao Shao, Hongyu Zhou, Katja Schwarz, and Yiyi Liao. ReRoPE: Repurposing RoPE for relative camera control.arXiv preprint arXiv:2602.08068, 2026. 2, 4, 6

    work page arXiv 2026

  11. [11]

    Step1X-3D: Towards high-fidelity and controllable generation of textured 3D assets

    Weiyu Li, Xuanyang Zhang, Zheng Sun, Di Qi, Hao Li, Wei Cheng, Weiwei Cai, Shihao Wu, Jiarui Liu, Zihao Wang, et al. Step1X-3D: Towards high-fidelity and controllable generation of textured 3D assets. arXiv preprint arXiv:2505.07747, 2025. 3

    work page arXiv 2025

  12. [12]

    TripoSG: High-fidelity 3D shape synthesis using large-scale rectified flow models.TPAMI, 2025

    Yangguang Li, Zi-Xin Zou, Zexiang Liu, Dehu Wang, Yuan Liang, Zhipeng Yu, Xingchao Liu, Yuan-Chen Guo, Ding Liang, Wanli Ouyang, et al. TripoSG: High-fidelity 3D shape synthesis using large-scale rectified flow models.TPAMI, 2025. 3

    2025

  13. [13]

    SS4D: Native 4D generative model via structured spacetime latents.TOG, 2025

    Zhibing Li, Mengchen Zhang, Tong Wu, Jing Tan, Jiaqi Wang, and Dahua Lin. SS4D: Native 4D generative model via structured spacetime latents.TOG, 2025. 2, 3, 4, 6, 7, 8, 14

    2025

  14. [14]

    Align your Gaussians: Text-to-4D with dynamic 3D Gaussians and composed diffusion models

    Huan Ling, Seung Wook Kim, Antonio Torralba, Sanja Fidler, and Karsten Kreis. Align your Gaussians: Text-to-4D with dynamic 3D Gaussians and composed diffusion models. InProc. CVPR, 2024. 2

    2024

  15. [15]

    Free4D: Tuning-free 4D scene generation with spatial-temporal consistency

    Tianqi Liu, Zihao Huang, Zhaoxi Chen, Guangcong Wang, Shoukang Hu, Liao Shen, Huiqiang Sun, Zhiguo Cao, Wei Li, and Ziwei Liu. Free4D: Tuning-free 4D scene generation with spatial-temporal consistency. InProc. ICCV, 2025. 2, 3

    2025

  16. [16]

    Decoupled Weight Decay Regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization.arXiv preprint arXiv:1711.05101, 2017. 6

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  17. [17]

    DreamFusion: Text-to-3D using 2D Diffusion

    Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. DreamFusion: Text-to-3D using 2D diffusion.arXiv preprint arXiv:2209.14988, 2022. 2 10

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  18. [18]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. InProc. ICML, 2021. 8

    2021

  19. [19]

    DreamGaussian4D: Generative 4D gaussian splatting.arXiv preprint arXiv:2312.17142, 2023

    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, 2023. 2

    work page arXiv 2023

  20. [20]

    L4GM: Large 4D Gaussian reconstruction model

    Jiawei Ren, Kevin Xie, Ashkan Mirzaei, Hanxue Liang, Xiaohui Zeng, Karsten Kreis, Ziwei Liu, Antonio Torralba, Sanja Fidler, Seung W Kim, et al. L4GM: Large 4D Gaussian reconstruction model. InProc. NeurIPS, 2024. 3

    2024

  21. [21]

    ActionMesh: Animated 3D mesh generation with temporal 3D diffusion

    Remy Sabathier, David Novotny, Niloy J Mitra, and Tom Monnier. ActionMesh: Animated 3D mesh generation with temporal 3D diffusion. InProc. CVPR, 2026. 2, 3, 4, 6, 7, 8, 14, 18

    2026

  22. [22]

    Text-to-4D dynamic scene generation.arXiv preprint arXiv:2301.11280, 2023

    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 dynamic scene generation.arXiv preprint arXiv:2301.11280, 2023. 2

    work page arXiv 2023

  23. [23]

    RoFormer: Enhanced transformer with rotary position embedding.Neurocomputing, 2024

    Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. RoFormer: Enhanced transformer with rotary position embedding.Neurocomputing, 2024. 2, 5

    2024

  24. [24]

    DimensionX: Create any 3D and 4D scenes from a single image with decoupled video diffusion

    Wenqiang Sun, Shuo Chen, Fangfu Liu, Zilong Chen, Yueqi Duan, Jun Zhu, Jun Zhang, and Yikai Wang. DimensionX: Create any 3D and 4D scenes from a single image with decoupled video diffusion. InProc. ICCV, 2025. 2, 3

    2025

  25. [25]

    FVD: A new metric for video generation

    Thomas Unterthiner, Sjoerd Van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, and Sylvain Gelly. FVD: A new metric for video generation. 2019. 8

    2019

  26. [26]

    Wan: Open and Advanced Large-Scale Video Generative Models

    Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, Jianyuan Zeng, Jiayu Wang, Jingfeng Zhang, Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng, Shiwei Zhang, Siyang Sun, Tao Fang, T...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  27. [27]

    Vidu4D: Single generated video to high-fidelity 4D reconstruction with dynamic gaussian surfels

    Yikai Wang, Xinzhou Wang, Zilong Chen, Zhengyi Wang, Fuchun Sun, and Jun Zhu. Vidu4D: Single generated video to high-fidelity 4D reconstruction with dynamic gaussian surfels. InProc. NeurIPS, 2024. 2

    2024

  28. [28]

    Barron, and Aleksander Holynski

    Rundi Wu, Ruiqi Gao, Ben Poole, Alex Trevithick, Changxi Zheng, Jonathan T. Barron, and Aleksander Holynski. CAT4D: Create anything in 4D with multi-view video diffusion models. InProc. CVPR, 2025. 2, 3

    2025

  29. [29]

    Direct3D-S2: Gigascale 3D generation made easy with spatial sparse attention

    Shuang Wu, Youtian Lin, Feihu Zhang, Yifei Zeng, Yikang Yang, Yajie Bao, Jiachen Qian, Siyu Zhu, Xun Cao, Philip Torr, et al. Direct3D-S2: Gigascale 3D generation made easy with spatial sparse attention. arXiv preprint arXiv:2505.17412, 2025. 3

    work page arXiv 2025

  30. [30]

    Native and Compact Structured Latents for 3D Generation

    Jianfeng Xiang, Xiaoxue Chen, Sicheng Xu, Ruicheng Wang, Zelong Lv, Yu Deng, Hongyuan Zhu, Yue Dong, Hao Zhao, Nicholas Jing Yuan, et al. Native and compact structured latents for 3D generation.arXiv preprint arXiv:2512.14692, 2025. 2, 3, 5, 6, 7, 8

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  31. [32]

    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. InProc. CVPR, 2025. 3

    2025

  32. [33]

    ULIP: Learning a unified representation of language, images, and point clouds for 3D understanding

    Le Xue, Mingfei Gao, Chen Xing, Roberto Martín-Martín, Jiajun Wu, Caiming Xiong, Ran Xu, Juan Carlos Niebles, and Silvio Savarese. ULIP: Learning a unified representation of language, images, and point clouds for 3D understanding. InProc. CVPR, 2023. 2, 8

    2023

  33. [34]

    SV4D 2.0: Enhancing spatio-temporal consistency in multi-view video diffusion for high-quality 4D generation

    Chun-Han Yao, Yiming Xie, Vikram V oleti, Huaizu Jiang, and Varun Jampani. SV4D 2.0: Enhancing spatio-temporal consistency in multi-view video diffusion for high-quality 4D generation. InProc. ICCV,

  34. [35]

    ShapeGen4D: Towards high quality 4D shape generation from videos

    Jiraphon Yenphraphai, Ashkan Mirzaei, Jianqi Chen, Jiaxu Zou, Sergey Tulyakov, Raymond A Yeh, Peter Wonka, and Chaoyang Wang. ShapeGen4D: Towards high quality 4D shape generation from videos. In Proc. ICLR, 2026. 2, 3, 4, 6, 7, 8, 14

    2026

  35. [36]

    Sculpt4D: Generating 4D Shapes via Sparse-Attention Diffusion Transformers

    Minghao Yin, Wenbo Hu, Jiale Xu, Ying Shan, and Kai Han. Sculpt4D: Generating 4D shapes via sparse-attention diffusion transformers.arXiv preprint arXiv:2604.21592, 2026. 3

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  36. [37]

    4DGen: Grounded 4D content generation with spatial-temporal consistency.arXiv preprint arXiv:2312.17225, 2023

    Yuyang Yin, Dejia Xu, Zhangyang Wang, Yao Zhao, and Yunchao Wei. 4DGen: Grounded 4D content generation with spatial-temporal consistency.arXiv preprint arXiv:2312.17225, 2023. 2

    work page arXiv 2023

  37. [38]

    GAvatar: Animatable 3D Gaussian avatars with implicit mesh learning

    Ye Yuan, Xueting Li, Yangyi Huang, Shalini De Mello, Koki Nagano, Jan Kautz, and Umar Iqbal. GAvatar: Animatable 3D Gaussian avatars with implicit mesh learning. InProc. CVPR, 2024. 2

    2024

  38. [39]

    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. InProc. ECCV, 2024. 2

    2024

  39. [40]

    3DShape2VecSet: A 3D shape represen- tation for neural fields and generative diffusion models.TOG, 2023

    Biao Zhang, Jiapeng Tang, Matthias Niessner, and Peter Wonka. 3DShape2VecSet: A 3D shape represen- tation for neural fields and generative diffusion models.TOG, 2023. 3

    2023

  40. [41]

    Gaussian variation field diffusion for high-fidelity video-to-4D synthesis.arXiv preprint arXiv:2507.23785,

    Bowen Zhang, Sicheng Xu, Chuxin Wang, Jiaolong Yang, Feng Zhao, Dong Chen, and Baining Guo. Gaussian variation field diffusion for high-fidelity video-to-4D synthesis.arXiv preprint arXiv:2507.23785,

    work page arXiv

  41. [42]

    4Diffusion: Multi-view video diffusion model for 4D generation

    Haiyu Zhang, Xinyuan Chen, Yaohui Wang, Xihui Liu, Yunhong Wang, and Yu Qiao. 4Diffusion: Multi-view video diffusion model for 4D generation. InProc. NeurIPS, 2024. 2

    2024

  42. [43]

    Texverse: A universe of 3D objects with high-resolution textures.arXiv preprint arXiv:2508.10868, 2025

    Yibo Zhang, Li Zhang, Rui Ma, and Nan Cao. Texverse: A universe of 3D objects with high-resolution textures.arXiv preprint arXiv:2508.10868, 2025. 6, 13

    work page arXiv 2025

  43. [44]

    Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

    Zibo Zhao, Zeqiang Lai, Qingxiang Lin, Yunfei Zhao, Haolin Liu, Shuhui Yang, Yifei Feng, Mingxin Yang, Sheng Zhang, Xianghui Yang, et al. Hunyuan3D 2.0: Scaling diffusion models for high resolution textured 3D assets generation.arXiv preprint arXiv:2501.12202, 2025. 3

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  44. [45]

    A unified approach for text- and image-guided 4D scene generation

    Yufeng Zheng, Xueting Li, Koki Nagano, Sifei Liu, Otmar Hilliges, and Shalini De Mello. A unified approach for text- and image-guided 4D scene generation. InProc. CVPR, 2024. 2

    2024

  45. [46]

    Uni3D: Exploring unified 3D representation at scale.arXiv preprint arXiv:2310.06773, 2023

    Junsheng Zhou, Jinsheng Wang, Baorui Ma, Yu-Shen Liu, Tiejun Huang, and Xinlong Wang. Uni3D: Exploring unified 3D representation at scale.arXiv preprint arXiv:2310.06773, 2023. 2, 8 12 Appendix Table A1:Ablation on the RoPE ratio.We evaluate on 32 held-out objects from TexVerse [ 43]. We use the full spatial RoPE setting (ratio = 1.0) as the reference and...

    work page arXiv 2023