wangjiongw / Bailando

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

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

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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.



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


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.


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

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

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

Step 3: Train motion GPT

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

Step 4: Actor-Critic finetuning on target music

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


To test with our pretrained models, please download the weights from here (Google Drive) or separately downloads the four weights from [weight 1]|[weight 2]|[weight 3]|[weight4] (坚果云) into ./experiments folder.

1. Generate dancing results

To test the VQ-VAE (with or without global shift as you indicated in config):

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

To test GPT:

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

To test final restuls:

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

2. Dance quality evaluations

After generating the dance in the above step, run the following codes.

Step 1: Extract the (kinetic & manual) features of all AIST++ motions (ONLY do it by once):


Step 2: compute the evaluation metrics:

python utils/

It will show exactly the same values reported in the paper. To fasten the computation, comment Line 184 of utils/ after computed the ground-truth feature once. To test another folder, change Line 182 to your destination, or kindly modify this code to a "non hard version" :)

Choreographic for music in the wild



    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 },


Our code is released under MIT License.

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

License:MIT License


Language:Python 98.9%Language:Shell 1.1%