Official PyTorch implementation of the paper "A Brand New Dance Partner:Music-Conditioned Pluralistic Dancing Synthesized by Multiple Dance Genres", CVPR 2022. Please visit our website for more details.

We propose MNET, a novel and scalable approach that can perform music-conditioned pluralistic dance generation synthesized by multiple dance genres using only a single model. Here, we learn a dance-genre aware latent representation by training a conditional generative adversarial network leveraging Transformer architecture.

# clone this repo:
git clone https://github.com/jw09191/MNET
cd MNET

# [OPTIONAL] create conda environment
conda create -n mnet python=3.8
conda activate mnet

# install requirements
pip install -r requirements.txt

In our experiment, we use AIST++. For more information about the dataset, please check this page. Please download the annotated motion datasets from here, and unzip them as ./data/AIST++/motions/ folder. Please download the music pieces (wav) from here, and unzip them as ./data/AIST++/wav/ folder. And please download SMPL models here, and copy the downloaded 'male' SMPL model to ./data/AIST++/SMPL_DIR/ folder.

# train on CPU
python train.py experiment=AISTPP trainer.gpus=0

# train on single GPU
python train.py experiment=AISTPP trainer.gpus=1

# train with DDP (Distributed data Parallel) (4 GPUs)
python train.py experiment=AISTPP trainer.gpus=4 +trainer.strategy=ddp

You can download pretrained models from here (Google Drive), and move them to ./logs/pretrain/ folder.

You can choose .pkl data from ./data/AIST++/motions and the generated dance videos are saved in ./logs/pretrain/demos/ folder.

# example
python demo.py -l pretrain -p gBR_sBM_cAll_d04_mBR0_ch01.pkl -t diversity 

# example
python demo.py -l pretrain -p gBR_sBM_cAll_d04_mBR0_ch01.pkl -t synthesis 

@InProceedings{Kim_2022_CVPR,
    author    = {Kim, Jinwoo and Oh, Heeseok and Kim, Seongjean and Tong, Hoseok and Lee, Sanghoon},
    title     = {A Brand New Dance Partner: Music-Conditioned Pluralistic Dancing Controlled by Multiple Dance Genres},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {3490-3500}
}

Our code is released under MIT License.