ShapeGen4D: Towards High Quality 4D Shape Generation from Videos
Authors: name: Jiraphon Yenphraphai, name: Ashkan Mirzaei, name: Jianqi Chen, name: Jiaxu Zou, name: Sergey Tulyakov, name: Raymond A. Yeh, name: Peter Wonka, name: Chaoyang Wang

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.