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SV4D 2.0: Enhancing Spatio-Temporal Consistency in Multi-View Video Diffusion for High-Quality 4D Generation

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arxiv 2503.16396 v3 pith:JALXR43F submitted 2025-03-20 cs.CV

Chun-Han Yao , Yiming Xie , Vikram Voleti , Huaizu Jiang , Varun Jampani This is my paper
classification cs.CV
keywords sv4dvideobetterconsistencytrainingcompareddatadetail
verification ladder T0 review T1 audit T2 compute T3 formal
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We present Stable Video 4D 2.0 (SV4D 2.0), a multi-view video diffusion model for dynamic 3D asset generation. Compared to its predecessor SV4D, SV4D 2.0 is more robust to occlusions and large motion, generalizes better to real-world videos, and produces higher-quality outputs in terms of detail sharpness and spatio-temporal consistency. We achieve this by introducing key improvements in multiple aspects: 1) network architecture: eliminating the dependency of reference multi-views and designing blending mechanism for 3D and frame attention, 2) data: enhancing quality and quantity of training data, 3) training strategy: adopting progressive 3D-4D training for better generalization, and 4) 4D optimization: handling 3D inconsistency and large motion via 2-stage refinement and progressive frame sampling. Extensive experiments demonstrate significant performance gain by SV4D 2.0 both visually and quantitatively, achieving better detail (-14\% LPIPS) and 4D consistency (-44\% FV4D) in novel-view video synthesis and 4D optimization (-12\% LPIPS and -24\% FV4D) compared to SV4D. Project page: https://sv4d20.github.io.

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Cited by 3 Pith papers

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

  1. 4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A diffusion model trained on 60,000 fitted 4D Gaussian Splatting human clips generates text-prompted, view-consistent dynamic humans directly in 4D, over 10x faster than video-first pipelines.

  2. WildSmoke: Ready-to-Use Dynamic 3D Smoke Assets from a Single Video in the Wild

    cs.CV 2025-09 conditional novelty 6.0 of 10

    WildSmoke reconstructs editable dynamic 3D smoke assets from a single in-the-wild video and reports a +2.22 dB average PSNR gain over prior fluid reconstruction methods on four real-world videos.

  3. 4DVD: Cascaded Dense-view Video Diffusion Model for High-quality 4D Content Generation

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A two-stage cascaded video diffusion model generates 16-view consistent videos from a monocular video, enabling higher-quality 4D content reconstruction.

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