FoMo: Generative 4D Morphing for Motion Tracking

We present FoMo, a generative model for 4D motion tracking of a known mesh from monocular video. We formulate the problem as generative 4D morphing natively in 4D vertex trajectory space with flow matching, with FoMo most notably resolving the resulting noise-to-vertex transport ambiguity through a dual-geometry architecture. FoMo significantly outperforms prior methods on mesh tracking and, when applied to skeletal tracking, also surpasses specialized skeletal trackers. It further generalizes to in-the-wild and synthetic videos, enabling tracking of reconstructed real-world meshes, cross-identity motion capture, and the use of video generation models as 4D motion generators.