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arXiv · 2510.06208

ShapeGen4D: Towards High Quality 4D Shape Generation from Videos

Jiraphon Yenphraphai·Ashkan Mirzaei·Jianqi Chen·Jiaxu Zou·Sergey Tulyakov·Raymond A. Yeh·Peter Wonka·Chaoyang Wang
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Abstract

Video-conditioned 4D shape generation aims to recover time-varying 3D geometry and view-consistent appearance directly from an input video. In this work, we introduce a native video-to-4D shape generation framework that synthesizes a single dynamic 3D representation end-to-end from the video. Our framework introduces three key components based on large-scale pre-trained 3D models: (i) a temporal attention that conditions generation on all frames while producing a time-indexed dynamic representation; (ii) a time-aware point sampling and 4D latent anchoring that promote temporally consistent geometry and texture; and (iii) noise sharing across frames to enhance temporal stability. Our method accurately captures non-rigid motion, volume changes, and even topological transitions without per-frame optimization. Across diverse in-the-wild videos, our method improves robustness and perceptual fidelity and reduces failure modes compared with the baselines.

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Jiraphon Yenphraphai, Ashkan Mirzaei, Jianqi Chen, Jiaxu Zou, Sergey Tulyakov, Raymond A. Yeh, Peter Wonka, Chaoyang Wang. 2025-10-07. ShapeGen4D: Towards High Quality 4D Shape Generation from Videos. https://arxiv.org/abs/2510.06208

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