TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting

Project Page | YouTube | Paper

This is the official PyTorch implementation of the CVPR 2020 paper "TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting".

Environment

conda install pytorch torchvision cudatoolkit=<your cuda version>
conda install pyyaml scikit-image scikit-learn opencv
pip install -r requirements.txt

Data

Mixamo

Download mixamo data here.
Extract under data/mixamo
run sh scripts/preprocess.sh

For directions for downloading Mixamo data please refer to this link.

Pretrained model

Download the mixamo pretrained model here.

Inference

For Skeleton Extraction, please consider using a pose estimation method such as OpenPose or DensePose. We require the input skeleton sequences to be in the format of a numpy .npy file:
- The file should contain an array with shape 15 x 2 x length.
- The first dimension (15) corresponds the 15 body joint defined here.
- The second dimension (2) corresponds to x and y coordinates.
- The third dimension (length) is the temporal dimension.
For Motion Retargeting Network, we provide the sample command for inference:

python infer_pair.py 
--config configs/transmomo.yaml 
--checkpoint transmomo_mixamo_36_800_24/checkpoints/autoencoder_00200000.pt # replace with actual path
--source a.npy  # replace with actual path
--target b.npy  # replace with actual path
--source_width 1280 --source_height 720 
--target_height 1920 --target_width 1080

For Skeleton-to-Video Rendering, please refer to Everybody Dance Now.

Training

To train the Motion Retargeting Network, run

python train.py --config configs/transmomo.yaml

Testing

For testing motion retargeting MSE, first generate the motion-retargeted motions with

python test.py
--config configs/transmomo.yaml
--checkpoint transmomo_mixamo_36_800_24/checkpoints/autoencoder_00200000.pt
--out_dir transmomo_mixamo_36_800_24_results # replace actual path to output directory

And then compute MSE by

python scripts/compute_mse.py 
--in_dir transmomo_mixamo_36_800_24_results # replace with the previous output directory

Project Structure

motion-disentangle-research.pytorch
├── configs - configuration files
├── data - place for storing data
├── docs - documentations
├── lib
│   ├── data.py - datasets and dataLoaders
│   ├── networks - encoders, decoders, discriminators, etc.
│   ├── trainer.py - training pipeline
│   ├── loss.py - loss functions
│   ├── operation.py - operations, e.g. rotation, projection, etc.
│   └── util - utility functions
├── out - place for storing output
├── infer_pair.py - perform motion retargeting
├── render_interpolate.py - perform motion and body interpolation
├── scripts - scripts for data processing and experiments
├── test.py - test MSE
└── train.py - main entrance for training

TODOs

Detailed documentation
Add example files
Release in-the-wild dancing video dataset (unannotated)
Tool for visualizing Mixamo test error
Tool for converting keypoint formats

Citation

Z. Yang*, W. Zhu*, W. Wu*, C. Qian, Q. Zhou, B. Zhou, C. C. Loy. "TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting." IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. (* indicates equal contribution.)

BibTeX:

@inproceedings{transmomo2020,
  title={TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting},
  author={Yang, Zhuoqian and Zhu, Wentao and Wu, Wayne and Qian, Chen and Zhou, Qiang and Zhou, Bolei and Loy, Chen Change},
  booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020}
}

Acknowledgement

This repository is partly based on Rundi Wu's Learning Character-Agnostic Motion for Motion Retargeting in 2D and Xun Huang's MUNIT: Multimodal UNsupervised Image-to-image Translation. The skeleton-to-rendering part is based on Everybody Dance Now. We sincerely thank them for their inspiration and contribution to the community.

zibozzb/transmomo.pytorch