Human3R: Everyone Everywhere All at Once

• URL: http://arxiv.org/abs/2510.06219v1
• Date: Tue, 07 Oct 2025 17:59:52 GMT
• Title: Human3R: Everyone Everywhere All at Once
• Authors: Yue Chen, Xingyu Chen, Yuxuan Xue, Anpei Chen, Yuliang Xiu, Gerard Pons-Moll,
• Abstract summary: We present Human3R, a feed-forward framework for online 4D human-scene reconstruction from monocular videos.<n>Human3R is a unified model that eliminates heavy dependencies and iterative refinement.<n>It delivers superior performance across tasks, including global human motion estimation, local human mesh recovery, video depth estimation, and camera pose estimation.
• Score: 69.16576238974876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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