Title: ShapeGen4D: Towards High Quality 4D Shape Generation from Videos
Primary area: generative models
Keywords: 4D reconstruction, generative model

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.