--- license: cc-by-nc-sa-4.0 pipeline_tag: image-to-3d --- # Human3R: Everyone Everywhere All at Once Human3R is a unified, feed-forward framework for online 4D human-scene reconstruction, in the world frame, from casually captured monocular videos. It jointly recovers global multi-person SMPL-X bodies ("everyone"), dense 3D scene ("everywhere"), and camera trajectories in a single forward pass ("all-at-once"). TL;DR: Inference with One model, One stage; Training in One day using One GPU * **Paper**: [Human3R: Everyone Everywhere All at Once](https://huggingface.co/papers/2510.06219) * **Project Page**: [https://fanegg.github.io/Human3R/](https://fanegg.github.io/Human3R/) * **Code**: [https://github.com/fanegg/Human3R](https://github.com/fanegg/Human3R)
Human3R Demo
## Sample Usage To run the inference demo, you can use the following command (assuming you have followed the installation steps from the [GitHub repository](https://github.com/fanegg/Human3R)): ```bash # input can be a folder or a video # the following script will run inference with Human3R and visualize the output with viser on port 8080 CUDA_VISIBLE_DEVICES=0 python demo.py --model_path MODEL_PATH --size 512 \ --seq_path SEQ_PATH --output_dir OUT_DIR --subsample 1 --use_ttt3r \ --vis_threshold 2 --downsample_factor 1 --reset_interval 100 # Example: CUDA_VISIBLE_DEVICES=0 python demo.py --model_path src/human3r.pth --size 512 --seq_path examples/GoodMornin1.mp4 --subsample 1 --use_ttt3r --vis_threshold 2 --downsample_factor 1 --reset_interval 100 --output_dir tmp ``` Output results will be saved to `output_dir`. ## Citation If you find our work useful, please cite: ```bibtex @article{chen2025human3r, title={Human3R: Everyone Everywhere All at Once}, author={Chen, Yue and Chen, Xingyu and Xue, Yuxuan and Chen, Anpei and Xiu, Yuliang and Gerard, Pons-Moll}, journal={arXiv preprint arXiv:2510.06219}, year={2025} } ```