# Human3R
**Repository Path**: zbgit11/Human3R
## Basic Information
- **Project Name**: Human3R
- **Description**: [ICLR 2026] An unified model for 4D human-scene reconstruction
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-08-19
- **Last Updated**: 2026-08-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](https://arxiv.org/abs/2510.06219)
[](https://fanegg.github.io/Human3R)
[](https://twitter.com/faneggchen) [](https://bsky.app/profile/fanegg.bsky.social)
[Yue Chen](https://fanegg.github.io/),
[Xingyu Chen](https://rover-xingyu.github.io/)*,
[Yuxuan Xue](https://yuxuan-xue.com/),
[Anpei Chen](https://apchenstu.github.io/),
[Yuliang Xiu](https://xiuyuliang.cn/)†,
[Gerard Pons-Moll](https://virtualhumans.mpi-inf.mpg.de/)
TL;DR: Inference with One model, One stage; Training in One day using One GPU
https://github.com/user-attachments/assets/47fc7ecf-5235-471c-84b9-ccfeca6d56ea
## Getting Started
### Installation
1. Clone Human3R.
```bash
git clone https://github.com/fanegg/Human3R.git
cd Human3R
```
2. Create the environment.
```bash
conda create -n human3r python=3.11 cmake
conda activate human3r
conda install pytorch torchvision pytorch-cuda=12.4 -c pytorch -c nvidia # use the correct version of cuda for your system
pip install -r requirements.txt
# issues with pytorch dataloader, see https://github.com/pytorch/pytorch/issues/99625
conda install 'llvm-openmp<16'
# for training logging
conda install -y gcc_linux-64 gxx_linux-64
pip install git+https://github.com/nerfstudio-project/gsplat.git
# for evaluation
pip install evo
pip install open3d
```
3. Compile the cuda kernels for RoPE (as in CroCo v2).
```bash
cd src/croco/models/curope/
python setup.py build_ext --inplace
cd ../../../../
```
### Download
Run the following commands to download all models and checkpoints into the `src/` directory. The first command will prompt you to register and log in to access each version of SMPL.
```Bash
# SMPLX family models
bash scripts/fetch_smplx.sh
# Human3R checkpoints
huggingface-cli download faneggg/human3r human3r_896L.pth --local-dir ./src
```
### Inference Demo
To run the inference demo, you can use the following command:
```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:
# To save the results, append `--save --output_dir tmp` to the command.
CUDA_VISIBLE_DEVICES=0 python demo.py --model_path src/human3r_896L.pth \
--size 512 --seq_path examples/GoodMornin1.mp4 \
--subsample 1 --use_ttt3r --vis_threshold 2 \
--downsample_factor 1 --reset_interval 100
```
### Evaluation
Please refer to the [eval.md](docs/eval.md) for more details.
### Model Cards
Please refer to the [inference.md](docs/inference.md) for using different backbones.
### Training
Please refer to the [train.md](docs/train.md) for more details.
## Acknowledgements
Our code is based on the following awesome repositories:
- [CUT3R](https://github.com/CUT3R/CUT3R), [TTT3R](https://github.com/Inception3D/TTT3R), [Multi-HMR](https://github.com/naver/multi-hmr), [PromptHMR](https://github.com/yufu-wang/PromptHMR), [GVHMR](https://github.com/zju3dv/GVHMR), [MonST3R](https://github.com/Junyi42/monst3r.git), [Easi3R](https://github.com/Inception3D/Easi3R), [DUSt3R](https://github.com/naver/dust3r), [Viser](https://github.com/nerfstudio-project/viser), [BEDLAM](https://github.com/pixelite1201/BEDLAM)
We thank the authors for releasing their code!
## 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}
}
```