Duolando

Code for ICLR 2024 paper "Duolando: Follower GPT with Off-Policy Reinforcement Learning for Dance Accompaniment."

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

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We introduce a novel task within the field of human motion generation, termed dance accompaniment, which necessitates the generation of responsive movements from a dance partner, the "follower", synchronized with the lead dancer’s movements and the underlying musical rhythm. Unlike existing solo or group dance generation tasks, a duet dance scenario entails a heightened degree of interaction between the two participants, requiring delicate coordination in both pose and position. To support this task, we first build a large-scale and diverse duet interactive dance dataset, DD100, by recording about 117 minutes of professional dancers’ performances. To address the challenges inherent in this task, we propose a GPT based model, Duolando, which autoregressively predicts the subsequent tokenized motion conditioned on the coordinated information of the music, the leader’s and the follower’s movements. To further enhance the GPT’s capabilities of generating stable results on unseen conditions (music and leader motions), we devise an off-policy reinforcement learning strategy that allows the model to explore viable trajectories from out-of-distribution samplings, guided by human-defined rewards. Based on the collected dataset and proposed method, we establish a benchmark with several carefully designed metrics.

Code

Environment setup

conda create -n duet python=3.8
conda activate duet
conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.1 -c pytorch
pip install -r requirement.txt

Besides, thie project needs the SMPLX models, please download (after register) from here. Please decompress it to a specific path. If you also wish to visualize the SMPLX models, please download Blender from here (we use version 3.4.1) and decompress it to './software'; please download SMPLX Add-on for Blender from here as a zip file.

DD100 Data

In this work, we collect a duet dance dataset, named DD100. Please visit here to download it and decompress to the ./data folder. The dataset will be look like

data
  |-- motion
  |     |_smplx
  |          |_all
  |             |-- [GENRE]_[CHOREOGRAPH]_[TAKE]_00.npy
  |             |-- [GENRE]_[CHOREOGRAPH]_[TAKE]_01.npy
  |             ... 
  |
  |__ music
        |_mp3
           |_all
              |-- [GENRE]_[CHOREOGRAPH]_[TAKE].mp3
              ...

Here, [GENRE] is the type of dance, like Ballet, Waltz, Tango, ... [CHOREOGRAPH] is the choreography index and [TAKE] represents the take index. For 67 dance sequences we recorded the movement twice. Since the details of the movement may change, we keep them in two different takes. The xxx_00.npy and xxx_01.npy are the SMPLX sequences of the lady (follower dacer) and the the man (leader), respectively.

In our experiment, we need first transfer SMPLX format into ratation matrix and 3d positions of the joints, and transfer the .mp3 music to specific features. Run the following scripts to do so. Before that, please change the 'model_path' in Line 19 of _prepare_motion_data.py to path/to/downloaded/smplx/models.

python _train_test_split.py
python _prepare_motion_data.py
python _prepre_music_data.py

Then you will see the split of 'train' and 'test' folder in subfolders under 'motion' and 'music', and see the extracted features. The 'pos3d' sequences are in shape of Tx55x3, where T is the frame number, 55 is the SMPLX joint number and 3 is (x,y,z) dimensions, while for 'rotmat', they are Tx55x9, where 9 is the 3x3 rotation matrix transformed from the axis angles of SMPLX. If you are not willing to do the preprocessing by yourself, you can directly download our preprocessed feature from here into ./data folder.

If you want to visualize these sequences, please run

bash visualize_01.sh 

Before that, please change Line 12 of tools/vis/vis_smplx_w_blender_01.py to path/to/smplx/add-on/zip, change Line 4 of visualize_01.sh to path/to/blender.

Training

The training of Duolando 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 in 'srun_xxx.sh'. A kind note here: if you do not have multiple gpus, you may need to lower the batch size in related config when training follower GPT and reinforcement learning part.

Step 1: Train pose motion VQ-VAE

bash srun_mix.sh configs/sep_vqvaexm_full_final.yaml train [node name] 1

Step 2: Train translation VQ-VAE

bash srun_transl.sh configs/transl_vqvaex_final.yaml train [node name] 1

Step 3: Train follower GPT

bash srun_gpt2t.sh configs/follower_gpt_beta0.9_final.yaml train [node name] 8

Step 4: RL finetuning

bash srun_rl_new_.sh configs/rl_final_debug_reward3_random_5mem_lr3e-5.yaml train [node name] 8

Evaluation

To test with our pretrained models, please download the weights from here (Google Drive) or [here] (OneDrive, TBD) into ./experiments folder.

1. Generate dancing results

To test follower GPT:

bash srun_gpt2t.sh configs/follower_gpt_beta0.9_final.yaml eval [node name] 1

To test follower GPT w. RL:

bash srun_rl_new_.sh configs/rl_final_debug_reward3_random_5mem_lr3e-5.yaml eval [node name] 1

2. Dance quality evaluations

a. Solo metrics

Run

python utils/metrics.py

To fasten the computation, comment the code "calc_and_save_feats(gt_root)" of line 215 in utils/metrics.py after computed the ground-truth feature once. To test another folder, change Line 311 to your destination.

b. Interactive metrics

python utils/metrics_duet.py

c. Contact frequency

This metric is computed based on torch-mesh-isect (but modified). We first inflate the smplx models vertices toward their normals by 1cm and then compute the intersection between the meshes. To do so, please first build it

cd utils/contact/torch-mesh-isect
python setup.py install
cd ../../..

Then, run

python utils/compute_contact_freq.py --root experiments/rl/eval/npy/ep0050

Before that, please change Line 80 and 81 to path/to/downloaded/smplx/models. This computation is slow.

The values should be exactly the same as reported in the paper.

d. Slipping rate

python utils/metric_footskating_dur.py

Accompaniment in the wild

A rough guidance to make an AR use in the wild. TBD

Wish you enjoy it!

Citation

@inproceedings{siyao2024duolando,
    title={Duolando: Follower GPT with Off-Policy Reinforcement Learning for Dance Accomapniment,
    author={Siyao, Li and Gu, Tianpei and Yang, Zhitao and Lin, Zhengyu and Liu, Ziwei and Ding, Henghui and Yang, Lei and Loy, Chen Change},
    booktitle={ICLR},
    year={2024}
}

License

This project is licensed under NTU S-Lab License 1.0. Redistribution and use should follow this license.