# hamer

**Repository Path**: techwolf/hamer

## Basic Information

- **Project Name**: hamer
- **Description**: https://github.com/geopavlakos/hamer
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-08-25
- **Last Updated**: 2025-08-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# HaMeR: Hand Mesh Recovery
Code repository for the paper:
**Reconstructing Hands in 3D with Transformers**

[Georgios Pavlakos](https://geopavlakos.github.io/), [Dandan Shan](https://ddshan.github.io/), [Ilija Radosavovic](https://people.eecs.berkeley.edu/~ilija/), [Angjoo Kanazawa](https://people.eecs.berkeley.edu/~kanazawa/), [David Fouhey](https://cs.nyu.edu/~fouhey/), [Jitendra Malik](http://people.eecs.berkeley.edu/~malik/)

[![arXiv](https://img.shields.io/badge/arXiv-2312.05251-00ff00.svg)](https://arxiv.org/pdf/2312.05251.pdf)  [![Website shields.io](https://img.shields.io/website-up-down-green-red/http/shields.io.svg)](https://geopavlakos.github.io/hamer/)     [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1rQbQzegFWGVOm1n1d-S6koOWDo7F2ucu?usp=sharing)  [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/geopavlakos/HaMeR)

![teaser](assets/teaser.jpg)

## News

- [2024/06] HaMeR received the 2nd place award in the Ego-Pose Hands task of the Ego-Exo4D Challenge! Please check the [validation report](https://www.cs.utexas.edu/~pavlakos/hamer/resources/egoexo4d_challenge.pdf).
- [2024/05] We have released the evaluation pipeline!
- [2024/05] We have released the HInt dataset annotations! Please check [here](https://github.com/ddshan/hint).
- [2023/12] Original release!

## Installation
First you need to clone the repo:
```
git clone --recursive https://github.com/geopavlakos/hamer.git
cd hamer
```

We recommend creating a virtual environment for HaMeR. You can use venv:
```bash
python3.10 -m venv .hamer
source .hamer/bin/activate
```

or alternatively conda:
```bash
conda create --name hamer python=3.10
conda activate hamer
```

Then, you can install the rest of the dependencies. This is for CUDA 11.7, but you can adapt accordingly:
```bash
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu117
pip install -e .[all]
pip install -v -e third-party/ViTPose
```

You also need to download the trained models:
```bash
bash fetch_demo_data.sh
```

Besides these files, you also need to download the MANO model. Please visit the [MANO website](https://mano.is.tue.mpg.de) and register to get access to the downloads section.  We only require the right hand model. You need to put `MANO_RIGHT.pkl` under the `_DATA/data/mano` folder.

### Docker Compose

If you wish to use HaMeR with Docker, you can use the following command:

```
docker compose -f ./docker/docker-compose.yml up -d
```

After the image is built successfully, enter the container and run the steps as above:

```
docker compose -f ./docker/docker-compose.yml exec hamer-dev /bin/bash
```

Continue with the installation steps:

```bash
bash fetch_demo_data.sh
```

## Demo
```bash
python demo.py \
    --img_folder example_data --out_folder demo_out \
    --batch_size=48 --side_view --save_mesh --full_frame
```

## HInt Dataset
We have released the annotations for the HInt dataset. Please follow the instructions [here](https://github.com/ddshan/hint)

## Training
First, download the training data to `./hamer_training_data/` by running:
```
bash fetch_training_data.sh
```

Then you can start training using the following command:
```
python train.py exp_name=hamer data=mix_all experiment=hamer_vit_transformer trainer=gpu launcher=local
```
Checkpoints and logs will be saved to `./logs/`.

## Evaluation
Download the [evaluation metadata](https://www.dropbox.com/scl/fi/7ip2vnnu355e2kqbyn1bc/hamer_evaluation_data.tar.gz?rlkey=nb4x10uc8mj2qlfq934t5mdlh) to `./hamer_evaluation_data/`. Additionally, download the FreiHAND, HO-3D, and HInt dataset images and update the corresponding paths in  `hamer/configs/datasets_eval.yaml`.

Run evaluation on multiple datasets as follows, results are stored in `results/eval_regression.csv`. 
```bash
python eval.py --dataset 'FREIHAND-VAL,HO3D-VAL,NEWDAYS-TEST-ALL,NEWDAYS-TEST-VIS,NEWDAYS-TEST-OCC,EPICK-TEST-ALL,EPICK-TEST-VIS,EPICK-TEST-OCC,EGO4D-TEST-ALL,EGO4D-TEST-VIS,EGO4D-TEST-OCC'
```

Results for HInt are stored in `results/eval_regression.csv`. For [FreiHAND](https://github.com/lmb-freiburg/freihand) and [HO-3D](https://codalab.lisn.upsaclay.fr/competitions/4318) you get as output a `.json` file that can be used for evaluation using their corresponding evaluation processes.

## Acknowledgements
Parts of the code are taken or adapted from the following repos:
- [4DHumans](https://github.com/shubham-goel/4D-Humans)
- [SLAHMR](https://github.com/vye16/slahmr)
- [ProHMR](https://github.com/nkolot/ProHMR)
- [SPIN](https://github.com/nkolot/SPIN)
- [SMPLify-X](https://github.com/vchoutas/smplify-x)
- [HMR](https://github.com/akanazawa/hmr)
- [ViTPose](https://github.com/ViTAE-Transformer/ViTPose)
- [Detectron2](https://github.com/facebookresearch/detectron2)

Additionally, we thank [StabilityAI](https://stability.ai/) for a generous compute grant that enabled this work.

## Open-Source Contributions
- [Wentao Hu](https://vincenthu19.github.io/) integrated the hand parameters predicted by HaMeR into SMPL-X - [Mano2Smpl-X](https://github.com/VincentHu19/Mano2Smpl-X)

## Citing
If you find this code useful for your research, please consider citing the following paper:

```bibtex
@inproceedings{pavlakos2024reconstructing,
    title={Reconstructing Hands in 3{D} with Transformers},
    author={Pavlakos, Georgios and Shan, Dandan and Radosavovic, Ilija and Kanazawa, Angjoo and Fouhey, David and Malik, Jitendra},
    booktitle={CVPR},
    year={2024}
}
```