Multi-THuMBS: Multi-person Tracking of 3D Human Meshes Beyond Video Shots

Tracking multi-person 3D human meshes from in-the-wildvideos is a highly challenging problem due to complex interactions, fre-quent occlusions, and severe truncation inherent in unconstrained envi-ronments. While recent approaches have improved robustness againstthese issues, they largely overlook the critical challenge prevalent inreal-world footage: frequent shot changes. These abrupt transitions incamera viewpoints often cause existing methods to lose track of humanidentities and fail in reconstructing temporally coherent trajectories. Al-though several recent works have explored 3D human mesh tracking un-der shot changes, they are still limited to single-person scenarios, mak-ing them inadequate for real-world videos where multiple people interactand appear simultaneously. To address this limitation, we propose Multi-THuMBS (Multi-person Tracking of 3D Human Meshes Beyond VideoShots) that leverages a state-of-the-art 3D scene prior to reconstructthe two boundary frames in a single shared 3D space. Human meshesare then registered within the shared 3D space, maintaining per-personidentity and motion consistency across shot changes. Extensive experi-ments demonstrate that our approach yields significant improvements in3D human mesh recovery, camera pose estimation, and identity track-ing, thereby ensuring high-fidelity motion reconstruction with consistentidentity preservation across shots compared to previous state-of-the-artmethods.