Bailando

Code for CVPR 2022 (oral) paper "Bailando: 3D dance generation via Actor-Critic GPT with Choreographic Memory"

[Paper] | [Project Page] | [Video Demo]

Driving 3D characters to dance following a piece of music is highly challenging due to the spatial constraints applied to poses by choreography norms. In addition, the generated dance sequence also needs to maintain temporal coherency with different music genres. To tackle these challenges, we propose a novel music-to-dance framework, Bailando, with two powerful components: 1) a choreographic memory that learns to summarize meaningful dancing units from 3D pose sequence to a quantized codebook, 2) an actor-critic Generative Pre-trained Transformer (GPT) that composes these units to a fluent dance coherent to the music. With the learned choreographic memory, dance generation is realized on the quantized units that meet high choreography standards, such that the generated dancing sequences are confined within the spatial constraints. To achieve synchronized alignment between diverse motion tempos and music beats, we introduce an actor-critic-based reinforcement learning scheme to the GPT with a newly-designed beat-align reward function. Extensive experiments on the standard benchmark demonstrate that our proposed framework achieves state-of-the-art performance both qualitatively and quantitatively. Notably, the learned choreographic memory is shown to discover human-interpretable dancing-style poses in an unsupervised manner.

Code

Environment

PyTorch == 1.6.0

Data preparation

In our experiments, we use AIST++ for both training and evaluation. Please visit here to download the AIST++ annotations and unzip them as './aist_plusplus_final/' folder, visit here to download all original music pieces (wav) into './aist_plusplus_final/all_musics'. And please set up the AIST++ API from here and download the required SMPL models from here. Please make a folder './smpl' and copy the downloaded 'male' SMPL model (with '_m' in name) to 'smpl/SMPL_MALE.pkl' and finally run

./prepare_aistpp_data.sh

to produce the features for training and test. Otherwise, directly download our preprocessed feature from here as ./data folder if you don't wish to process the data.

Training

The training of Bailando comprises of 4 steps in the following sequence. If you are using the slurm workload manager, you can directly run the corresponding shell. Otherwise, please remove the 'srun' parts. Our models are all trained with single NVIDIA V100 GPU. * A kind reminder: the quantization code does not fit multi-gpu training

Step 1: Train pose VQ-VAE (without global velocity)

sh srun.sh configs/sep_vqvae.yaml train [your node name] 1

Step 2: Train glabal velocity branch of pose VQ-VAE

sh srun.sh configs/sep_vavqe_root.yaml train [your node name] 1

Step 3: Train motion GPT

sh srun_gpt_all.sh configs/cc_motion_gpt.yaml train [your node name] 1

Step 4: Actor-Critic finetuning on target music

sh srun_actor_critic.sh configs/actor_critic.yaml train [your node name] 1

Evaluation

To test with our pretrained models, please download the weights from here (Google Drive) or here (坚果云) as ./experiments folder.

1. Generate dancing results

sh srun_xxx.sh configs/xxx.yaml eval [your node name] 1

2. Dance quality evaluations

TODO

Choreographic for music in the wild

TODO

Citation

@inproceedings{siyao2022bailando,
    title={Bailando: 3D dance generation via Actor-Critic GPT with Choreographic Memory,
    author={Siyao, Li and Yu, Weijiang and Gu, Tianpei and Lin, Chunze and Wang, Quan and Qian, Chen and Loy, Chen Change and Liu, Ziwei },
    booktitle={CVPR},
    year={2022}
}

License

Our code is released under MIT License.