CoMotion: Concurrent Multi-person 3D Motion
Authors: name: Alejandro Newell; name: Peiyun Hu; name: Lahav Lipson; name: Stephan R. Richter; name: Vladlen Koltun
Published: 2025-04-16 15:40:15

Abstract: We introduce an approach for detecting and tracking detailed 3D poses of
multiple people from a single monocular camera stream. Our system maintains
temporally coherent predictions in crowded scenes filled with difficult poses
and occlusions. Our model performs both strong per-frame detection and a
learned pose update to track people from frame to frame. Rather than match
detections across time, poses are updated directly from a new input image,
which enables online tracking through occlusion. We train on numerous image and
video datasets leveraging pseudo-labeled annotations to produce a model that
matches state-of-the-art systems in 3D pose estimation accuracy while being
faster and more accurate in tracking multiple people through time. Code and
weights are provided at https://github.com/apple/ml-comotion