Human3R: Everyone Everywhere All at Once
Authors: name: Yue Chen, name: Xingyu Chen, name: Yuxuan Xue, name: Anpei Chen, name: Yuliang Xiu, name: Gerard Pons-Moll

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