Style-ERD: Responsive and Coherent Online Motion Style Transfer.

This repository contains code for the CVPR 2022 paper Style-ERD: Responsive and Coherent Online Motion Style Transfer.

Abstract:

Motion style transfer is a common method for enriching character animation. Motion style transfer algorithms are often designed for offline settings where motions are processed in segments. However, for online animation applications, such as real-time avatar animation from motion capture, motions need to be processed as a stream with minimal latency. In this work, we realize a flexible, high-quality motion style transfer method for this setting. We propose a novel style transfer model, Style-ERD, to stylize motions in an online manner with an Encoder-Recurrent-Decoder structure, along with a novel discriminator that combines feature attention and temporal attention. Our method stylizes motions into multiple target styles with a unified model. Although our method targets online settings, it outperforms previous offline methods in motion realism and style expressiveness and provides significant gains in runtime efficiency.

Usage of the code

Install the dependencies

To install the python dependencies, you can create a new virtual environment than install the necessary packages:

python3 -m venv python_env
source python_env/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

This setup is tested on Ubuntu 18.04 with Python 3.6.9

Data preprocessing

Currently, a few motion capture clips are stored in data/xia_test for basic testing. If you are looking for more data, you can download it from Google Drive. Then unzip the downloaded .zip to data

Test

There is a pretrained model saved under experiments/pretrained_model

To reproduce the results shown in the paper, you can run the following command:

python3 test.py --load_dir experiments/pretrained_model --target_style proud --input_motion data/xia_test/neutral_01_000_walk.bvh --input_content walk --input_style neutral --no_pos

Training

You need to download the complete dataset from the link shown above to train the models.

To train the Style-ERD model, you can run the following command:

python3 train.py --perceptual_loss --no_pos --dis_lr 5e-5 --w_reg 128 --n_epoch 2000 --tag train_Style_ERD

To train the content-classification network used by the content supervision module, you can run:

python3 train.py --train_classifier --n_epoch 1000

Then you need to move the best model to data and name the saved model as classifier.pt

Acknowlegements

The code builds upon from the following publications:

The dataset is provided by:

Citation

Please consider citing our paper in your publications if the project helps your research.

@inproceedings{tao2022style,
  title={Style-ERD: Responsive and Coherent Online Motion Style Transfer},
  author={Tao, Tianxin and Zhan, Xiaohang and Chen, Zhongquan and van de Panne, Michiel},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6593--6603},
  year={2022}
}

PerfectBlueFeynman/Online-Motion-Style-Transfer