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[CVPR 2026] A versatile toolbox for human mesh estimation

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# Expressive and Versatile Avatar Tracker

This repository serves as a comprehensive and flexible toolbox for human mesh estimation. It supports multiple input modalities and workflows, including:

- Estimating proxy meshes from human-centric images and videos.
- Estimating SMPL-X parameters from colored meshes (experimental).
- Recovering SMPL-X parameters from a given SMPL-X mesh.

## Installation

Setting up the environment from scratch can be somewhat involved due to the dependencies required by this project. For convenience, we provide a pre-built Docker image on Docker Hub:

```
docker pull zjwfufu/eva-tracker:latest
```

After launching a container with this image, simply run:

```
source activate
conda activate eva_track
```

to enter the pre-configured environment.

> [!NOTE]
>
> A few components may still need to be re-installed:
>
> - Packages previously installed via `pip install -e .` should be re-installed inside the activated enviroment.
>
>   ```
>   # sam2
>   pip install -e ./modules/models/image_segmenter/sam2 --no-deps
>   
>   # osx modified mmpose
>   cd ./modules/models/mesh_estimator/osx/transformer_utils
>   pip install -e .
>   cd ../../../../../
>   pip install --upgrade setuptools
>   ```
>
> - PyTorch3D may need to be rebuilt depending on your GPU architecture and CUDA configuration.

If you prefer to install the environment manually, you can follow the step-by-step instructions below:

- Clone this repository and create conda environment

```
  git clone https://github.com/zjwfufu/eva_tracker
  
  conda create -n eva_track python=3.10
  ```

- Install PyTorch and PyTorch3D

```
  pip install torch==2.3.0 torchvision==0.18.0 --index-url https://download.pytorch.org/whl/cu118
  
  pip install -U xformers==0.0.26.post1 --index-url https://download.pytorch.org/whl/cu118
  
  pip install "git+https://github.com/facebookresearch/pytorch3d.git"
  ```

- Install other requirements

```
  pip install -r requirements.txt
  
  pip install chumpy==0.70 --no-build-isolation
  ```

- Install SAM2

```
  pip install -e ./modules/models/image_segmenter/sam2 --no-deps
  ```

- Install OSX dependencies

- Install mmcv-full==1.7.0

Install mmcv-full 1.7.0 compiled with C++17. You can download the source code from [this link](https://box.nju.edu.cn/f/be11ae5086554f8186f5/?dl=1), which has been patched to compile with C++17 during installation.

After downloading and extracting the archive, navigate to the source directory and run:

```
    MMCV_WITH_OPS=1 pip install . -v
    ```

- Install modified mmpose

```
    cd ./modules/models/mesh_estimator/osx/transformer_utils
    pip install -e .
    cd ../../../../../
    pip install --upgrade setuptools
    ```

- Install mmengine

```
    pip install mmengine==0.10.7
    ```

- Install TrustNCG optimizer

```
  pip install "git+https://github.com/vchoutas/torch-trust-ncg.git"
  ```

- Fix numpy version

```
  pip install numpy==1.23.0
  ```

## Weights

We provide a full archive of all the pretrained weights required by this project, available at [this link](https://box.nju.edu.cn/f/2badff51a4b14d4c8764/). Please download and extract the archive into `./modules/weights`. After extraction, the directory structure as follow:

```
./modules/weights/
├── face_tracker
│   ├── emica
│   │   ├── EMICA-CVT_flame2020_notexture.pt
│   │   └── ins_scrfd_10g_bnkps.onnx
│   ├── flame
│   │   ├── canonical.obj
│   │   └── FLAME_with_eye.pt
│   ├── mask2former
│   │   ├── config.json
│   │   ├── gitattributes
│   │   ├── model.safetensors
│   │   ├── preprocessor_config.json
│   │   ├── pytorch_model.bin
│   │   └── README.md
│   ├── matting
│   │   └── stylematte_synth.pt
│   └── vgghead
│       ├── lmks_2d.pt
│       └── vgg_heads_l.trcd
├── hand_tracker
│   ├── hamer.ckpt
├── human_template
│   ├── flame
│   │   ├── 2019
│   │   ├── flame_dynamic_embedding.npy
│   │   ├── FLAME_FEMALEl.pkl
│   │   ├── FLAME_MALE.pkl
│   │   ├── FLAME_NEUTRAL.pkl
│   │   ├── flame_static_embedding.pkl
│   │   ├── FLAME_texture.npz
│   │   └── Readme.pdf
│   ├── flame_assets
│   │   ├── flame
│   │   ├── flame_arkit_bs.npy
│   │   ├── pred_expression.json
│   │   ├── shoulder_mesh.obj
│   │   └── teeth_jawopen_offset.npy
│   ├── mano
│   │   ├── MANO_LEFT.pkl
│   │   └── MANO_RIGHT.pkl
│   ├── mano_mean_params.npz
│   ├── pose_estimate
│   │   └── multiHMR_896_L.pt
│   ├── smpl
│   │   ├── SMPL_FEMALE.pkl
│   │   ├── SMPL_MALE.pkl
│   │   └── SMPL_NEUTRAL.pkl
│   ├── smpl_mean_params.npz
│   ├── smplx
│   │   ├── MANO_SMPLX_vertex_ids.pkl
│   │   ├── SMPLX_FEMALE.npz
│   │   ├── SMPL-X__FLAME_vertex_ids.npy
│   │   ├── smplx_flip_correspondences.npz
│   │   ├── SMPLX_MALE.npz
│   │   ├── SMPLX_NEUTRAL.npz
│   │   ├── smplx_npz.zip
│   │   ├── SMPLX_to_J14.pkl
│   │   ├── smplx_uv
│   │   └── version.txt
│   └── smplx_points
├── image_matting
│   └── BiRefNet-general-epoch_244.pth
├── image_segmenter
│   └── sam2.1_hiera_large.pt
├── keypoint_detector
│   └── sapiens_1b_coco_wholebody_best_coco_wholebody_AP_727_torchscript.pt2
└── mesh_estimator
  ├── multiHMR_896_L.pt
  └── osx_l.pth.tar
```

## Usage

- ####  [Image Tracking](docs/image.md) 
- ####  [Video Tracking](docs/video.md) 
- ####  [SMPL-X Fitting](docs/fit.md) 
- ####  [Mesh Tracking](docs/mesh.md)

## Discussions

Estimating human proxy meshes (FLAME, MANO, SMPL-X) from monocular observations is inherently ill-posed and highly sensitive to initialization and priors.

**Estimator backbone.** Most pipelines rely on a pretrained HMR model to provide an initial SMPL-X estimate. However, many existing HMR models are primarily supervised with 2D signals during training, which introduces depth ambiguities. As a result, the predicted bodies may lean forward/backward, and the relative distances between hands, arms, and torso can become inconsistent, causing misalignment. More advanced models such as [SAM-3D-Body](https://github.com/facebookresearch/sam-3d-body) show promising potential for alleviating these issues.

**Temporal smoothness.** When tracking trimmed videos, temporal smoothness must be applied carefully. For tasks that only require per-frame accuracy, a simple second-order smoothness term is often sufficient. For applications like animation or cross reenactment, temporal regularization requires more careful tuning to avoid over-smoothing important dynamics.

**Blinking.** This project does not explicitly model eyelid poses. Some blink motions represented in FLAME’s expression blendshapes may be suppressed by strong smoothness terms and thus become less noticeable during optimization.

## Acknowledgement

This project is built on many amazing open-source projects:

- [LHM_track](https://github.com/aigc3d/LHM_Track)
- [VHAP](https://github.com/ShenhanQian/VHAP)
- [EHM_track](https://github.com/Pixel-Talk/GUAVA)
- [GAGAvatar_track](https://github.com/xg-chu/GAGAvatar_track/)
- [SMPL-X](https://github.com/vchoutas/smplx)

and many research works:

- [Multi-HMR](https://github.com/naver/multi-hmr)
- [Sapiens](https://github.com/facebookresearch/sapiens)
- [OSX](https://github.com/IDEA-Research/OSX)

Thanks all the authors for their great work.

## Cite

If you find this project useful in your research, please cite with the following BibTex entry:

```bibtex
@misc{zhang2025evatracker,
  title={Expressive and Versatile Avatar Tracker},
  author={Zhang, Jiawei},
  year={2025},
  month={dec},
  url={https://github.com/zjwfufu/EVA_tracker}
}
```
```bibtex
@article{zhang2025bringingportrait3dpresence,
  title={Bringing Your Portrait to 3D Presence},
  author={Zhang, Jiawei and Chu, Lei and Li, Jiahao and Zang, Zhenyu and Li, Chong and Li, Xiao and Cao, Xun and Zhu, Hao and Lu, Yan},
  journal={arXiv preprint arXiv:2511.22553},
  year={2025}
}
```

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