title: CoMotion: Concurrent Multi-person 3D Motion
summary: 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
paper_authors._id: 680764930b2f7fda706f6c03
paper_authors.name: Alejandro Newell
paper_authors._id: 680764930b2f7fda706f6c04
paper_authors.name: Peiyun Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-22T11:07:36.922Z
paper_authors._id: 66c0bff5453a7ef6c5b94cad
paper_authors.avatarUrl: /avatars/8d56e44718f7178e4578f6c17a697678.svg
paper_authors.fullname: Peiyun Hu
paper_authors.type: user
paper_authors.user: peiyun-hu-apple
paper_authors._id: 680764930b2f7fda706f6c05
paper_authors.name: Lahav Lipson
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-22T11:07:46.486Z
paper_authors._id: 65de060e8ad379149e3dbac6
paper_authors.avatarUrl: /avatars/ff48c3407547dd5fc51dcb1b961a5e4a.svg
paper_authors.fullname: Lahav Lipson
paper_authors.type: user
paper_authors.user: lahavl
paper_authors._id: 680764930b2f7fda706f6c06
paper_authors.name: Stephan R. Richter
paper_authors._id: 680764930b2f7fda706f6c07
paper_authors.name: Vladlen Koltun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-22T11:08:01.500Z
paper_authors._id: 6707b1a9356f027ca986d6b1
paper_authors.avatarUrl: /avatars/d32eae81b27dc4f7090196d410e96876.svg
paper_authors.fullname: Vladlen Koltun
paper_authors.type: user
paper_authors.user: vkoltun
paper_title: CoMotion: Concurrent Multi-person 3D Motion
paper_summary: 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
paper_ai_summary: A system detects and tracks 3D poses of multiple people in crowded scenes using a single camera, maintaining accuracy and speed even with occlusions.
paper_ai_keywords: 3D pose estimation monocular camera temporally coherent predictions pose update online tracking pseudo-labeled annotations