Enhance model card for Human3R: Add metadata, links, description, and usage

README.md

@@ -1,3 +1,47 @@
----
-license: cc-by-nc-sa-4.0
----
+---
+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)
+
+<div align="center">
+<img src="https://github.com/user-attachments/assets/47fc7ecf-5235-471c-84b9-ccfeca6d56ea" alt="Human3R Demo" width="100%">
+</div>
+
+## 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}
+    }
+```