Human3R: Everyone Everywhere All at Once
We present Human3R, a unified, feed-forward framework for online 4D
human-scene reconstruction, in the world frame, from casually captured
monocular videos. Unlike previous approaches that rely on multi-stage
pipelines, iterative contact-aware refinement between humans and scenes, and
heavy dependencies, e.g., human detection, depth estimation, and SLAM
pre-processing, Human3R jointly recovers global multi-person SMPL-X bodies
("everyone"), dense 3D scene ("everywhere"), and camera trajectories in a
single forward pass ("all-at-once"). Our method builds upon the 4D online
reconstruction model CUT3R, and uses parameter-efficient visual prompt tuning,
to strive to preserve CUT3R's rich spatiotemporal priors, while enabling direct
readout of multiple SMPL-X bodies. Human3R is a unified model that eliminates
heavy dependencies and iterative refinement. After being trained on the
relatively small-scale synthetic dataset BEDLAM for just one day on one GPU, it
achieves superior performance with remarkable efficiency: it reconstructs
multiple humans in a one-shot manner, along with 3D scenes, in one stage, at
real-time speed (15 FPS) with a low memory footprint (8 GB). Extensive
experiments demonstrate that Human3R delivers state-of-the-art or competitive
performance across tasks, including global human motion estimation, local human
mesh recovery, video depth estimation, and camera pose estimation, with a
single unified model. We hope that Human3R will serve as a simple yet strong
baseline, be easily extended for downstream applications.Code available in
https://fanegg.github.io/Human3R
68e608e8975ac4c405ef21a6
Yue Chen
claimed_verified
2025-10-11T14:04:58.554Z
66f80281d88dc2ad510663e9
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/5HV3mTu-cxPCnWlCi_2wB.jpeg
Yue Chen
user
faneggg
68e608e8975ac4c405ef21a7
Xingyu Chen
claimed_verified
2025-10-11T14:05:24.535Z
66606a13fc6c0816442bd161
https://cdn-avatars.huggingface.co/v1/production/uploads/66606a13fc6c0816442bd161/tS8pBDXEb3QkIjvZao55l.jpeg
Xingyu Chen
user
rover-xingyu
68e608e8975ac4c405ef21a8
Yuxuan Xue
68e608e8975ac4c405ef21a9
Anpei Chen
68e608e8975ac4c405ef21aa
Yuliang Xiu
claimed_verified
2025-10-09T06:51:40.157Z
6183d0b249ef1d984699e4a3
https://cdn-avatars.huggingface.co/v1/production/uploads/6183d0b249ef1d984699e4a3/M6B9b73v8su9H8jfdcbgu.jpeg
Yuliang Xiu
user
Yuliang
68e608e8975ac4c405ef21ab
Gerard Pons-Moll
Human3R: Everyone Everywhere All at Once
We present Human3R, a unified, feed-forward framework for online 4D
human-scene reconstruction, in the world frame, from casually captured
monocular videos. Unlike previous approaches that rely on multi-stage
pipelines, iterative contact-aware refinement between humans and scenes, and
heavy dependencies, e.g., human detection, depth estimation, and SLAM
pre-processing, Human3R jointly recovers global multi-person SMPL-X bodies
("everyone"), dense 3D scene ("everywhere"), and camera trajectories in a
single forward pass ("all-at-once"). Our method builds upon the 4D online
reconstruction model CUT3R, and uses parameter-efficient visual prompt tuning,
to strive to preserve CUT3R's rich spatiotemporal priors, while enabling direct
readout of multiple SMPL-X bodies. Human3R is a unified model that eliminates
heavy dependencies and iterative refinement. After being trained on the
relatively small-scale synthetic dataset BEDLAM for just one day on one GPU, it
achieves superior performance with remarkable efficiency: it reconstructs
multiple humans in a one-shot manner, along with 3D scenes, in one stage, at
real-time speed (15 FPS) with a low memory footprint (8 GB). Extensive
experiments demonstrate that Human3R delivers state-of-the-art or competitive
performance across tasks, including global human motion estimation, local human
mesh recovery, video depth estimation, and camera pose estimation, with a
single unified model. We hope that Human3R will serve as a simple yet strong
baseline, be easily extended for downstream applications.Code available in
https://fanegg.github.io/Human3R
Human3R is a unified, feed-forward framework for real-time 4D human-scene reconstruction from monocular videos, achieving state-of-the-art performance with a single model and minimal dependencies.
feed-forward framework 4D reconstruction monocular videos SMPL-X bodies dense 3D scene camera trajectories CUT3R parameter-efficient visual prompt tuning spatiotemporal priors real-time speed memory footprint global human motion estimation local human mesh recovery video depth estimation camera pose estimation