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