AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing (ECCV 2022, Official Code)

Jiaxi Jiang¹, Paul Streli¹, Huajian Qiu¹, Andreas Fender¹, Larissa Laich², Patrick Snape², Christian Holz¹

¹ Sensing, Interaction & Perception Lab, Department of Computer Science, ETH Zürich, Switzerland
² Reality Labs at Meta, Zurich, Switzerland

---

Overview

Contents

• Datasets
• Training
• Testing
• Pretrained Models
• Citation
• License and Acknowledgement

Datasets

1. Please download the datasets BMLrub, CMU, and HDM05 from AMASS.
2. Download the required body model and placed them in support_data/body_models directory of this repository. For SMPL+H body model, download it from http://mano.is.tue.mpg.de/. Please download the AMASS version of the model with DMPL blendshapes. You can obtain dynamic shape blendshapes, e.g. DMPLs, from http://smpl.is.tue.mpg.de
3. (Optional) If you want to have new random data split, run generate_split.py
4. Run prepare_data.py to preprocess the input data for faster training. The data split for training and testing data in our paper is stored under the folder data_split.

Training

For training, please run:

python main_train_avatarposer.py -opt options/train_avatarposer.json

Testing

For testing, please run:

python main_test_avatarposer.py

Pretrained Models

Click Pretrained Models to download our pretrained model for AvatarPoser, and put it into model_zoo.

Citation

If your find our paper or codes useful, please cite our work:

@inproceedings{jiang2022avatarposer,
  title={AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing},
  author={Jiang, Jiaxi and Streli, Paul and Qiu, Huajian and Fender, Andreas and Laich, Larissa and Snape, Patrick and Holz, Christian},
  booktitle={Proceedings of European Conference on Computer Vision},
  year={2022},
  organization={Springer}
}

License and Acknowledgement

This project is released under the MIT license. We refer to the code framework in FBCNN and KAIR for network training.