CoMotion: Concurrent Multi-person 3D Motion

ICLR 2025 Conference Submission1384 Authors

Keywords: human pose estimation, 3d human pose, tracking

TL;DR: Online multi-person 3D pose estimation and tracking in video with recurrent cross-attention

Abstract: We introduce an approach for 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. Rather than detect poses and associate them to current tracks, our model directly updates all tracked poses simultaneously given a new input image. We train on numerous single-image and video datasets with both 2D and 3D annotations to produce a model that matches the 3D pose estimation quality of state-of-the-art systems while performing faster and more accurate tracking on in-the-wild videos.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 1384