CoMotion: Concurrent Multi-person 3D Motion

Alejandro Newell, Peiyun Hu, Lahav Lipson, Stephan Richter, Vladlen Koltun

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 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.

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

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Submission Number: 1384