Shape of Motion: 4D Reconstruction from a Single Video
Authors: name: Qianqian Wang, name: Vickie Ye, name: Hang Gao, name: Jake Austin, name: Zhengqi Li, name: Angjoo Kanazawa

Monocular dynamic reconstruction is a challenging and long-standing vision
problem due to the highly ill-posed nature of the task. Existing approaches are
limited in that they either depend on templates, are effective only in
quasi-static scenes, or fail to model 3D motion explicitly. In this work, we
introduce a method capable of reconstructing generic dynamic scenes, featuring
explicit, full-sequence-long 3D motion, from casually captured monocular
videos. We tackle the under-constrained nature of the problem with two key
insights: First, we exploit the low-dimensional structure of 3D motion by
representing scene motion with a compact set of SE3 motion bases. Each point's
motion is expressed as a linear combination of these bases, facilitating soft
decomposition of the scene into multiple rigidly-moving groups. Second, we
utilize a comprehensive set of data-driven priors, including monocular depth
maps and long-range 2D tracks, and devise a method to effectively consolidate
these noisy supervisory signals, resulting in a globally consistent
representation of the dynamic scene. Experiments show that our method achieves
state-of-the-art performance for both long-range 3D/2D motion estimation and
novel view synthesis on dynamic scenes. Project Page:
https://shape-of-motion.github.io/