caijianfei / music2dance Goto Github PK

PyTorch implementation of the cross-modality generative model that synthesizes dance from music.

Hsin-Ying Lee, Xiaodong Yang, Ming-Yu Liu, Ting-Chun Wang, Yu-Ding Lu, Ming-Hsuan Yang, Jan Kautz
Dancing to Music Neural Information Processing Systems (NeurIPS) 2019
[Paper] [YouTube] [Project] [Blog] [Supp]

• Beat-Matching
  1st row: generated dance sequences, 2nd row: music beats, 3rd row: kinematics beats

• Multimodality
  Generate various dance sequences with the same music and the same initial pose.

• Long-Term Generation
  Seamlessly generate a dance sequence with arbitrary length.

• Photo-Realisitc Videos
  Map generated dance sequences to photo-realistic videos.

python train_decomp.py --name Decomp

python train_comp.py --name Decomp --decomp_snapshot DECOMP_SNAPSHOT

python demo.py --decomp_snapshot DECOMP_SNAPSHOT --comp_snapshot COMP_SNAPSHOT --aud_path AUD_PATH --out_file OUT_FILE --out_dir OUT_DIR --thr THR

• Flags

  • aud_path: input .wav file
  • out_file: location of output .mp4 file
  • out_dir: directory of output frames
  • thr: threshold based on motion magnitude
  • modulate: whether to do beat warping

• Example

python demo.py -decomp_snapshot snapshot/Stage1.ckpt --comp_snapshot snapshot/Stage2.ckpt --aud_path demo/demo.wav --out_file demo/out.mp4 --out_dir demo/out_frame

If you find this code useful for your research, please cite our paper:

@inproceedings{lee2019dancing2music,
  title={Dancing to Music},
  author={Lee, Hsin-Ying and Yang, Xiaodong and Liu, Ming-Yu and Wang, Ting-Chun and Lu, Yu-Ding and Yang, Ming-Hsuan and Kautz, Jan},
  booktitle={NeurIPS},
  year={2019}
}

Copyright (C) 2020 NVIDIA Corporation. All rights reserved. This work is made available under NVIDIA Source Code License (1-Way Commercial). To view a copy of this license, visit https://nvlabs.github.io/Dancing2Music/LICENSE.txt