FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance Generation (ICCV 2023)

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Teaser

Download the FineDance Dataset

We introduce a Fine-grained Choreography Dance dataset(FineDance). It comprises over 14.6 hours of data collected from 346 paired songs and dances, was created by 27 professional dancers and a motion capture system, which has accurate body and hand motions. The fine-grained 22 dance genres of FineDance spanning traditional and modern styles, which make the genre-matching of generated dance sequences and given music become more challenging. The part(7.7 hours) of FineDance dataset can be downloaded at BaiduNetDisk

Dataset Descriptions

Put the downloaded FineDance data into './data'.

The data directory is organized as follows:

label_json: contains the song name, coarse style and fine-grained genre.

motion: contains the SMPLH format motion data.

music_wav: contains the music data in 'wav' format.

music_npy: contains the music feature extracted by librosa follow AIST++

Here is an example python script to read the motion file

import numpy as np
data = np.load("motion/001.npy")
T, C = data.shape           # T is the number of frames
smpl_poses = data[:, 3:]
smpl_trans = data[:, :3]

Prepare

Download the assets files from Google Drive.

Install the conda enviroment.

conda env create -f environment.yaml
conda activate FineNet

Data preprocessing

python data/code/pre_motion.py

Training

accelerate launch train_seq.py

Generate

python data/code/slice_music_motion.py
python generate_all.py  --motion_save_dir generated/finedance_seq_120_dancer --save_motions

Generate dance by custom music

python test.py --music_dir 'your music dir' --save_motions

Visualization

python render.py --modir eval/motions --gpu 0

Dataset split

We spilt FineDance dataset into train, val and test sets in two ways: FineDance@Genre and FineDance@Dancer. Each music and paired dance are only present in one split.

1. The test set of FineDance@Genre includes a broader range of dance genres, but the same dancer appear in train/val/test set. Although the training set and test set include the same dancers, the same motions do not appear in both the training and testing sets. This is because these dancers do not have distinct personal characteristics in their dances.
2. The train/val/test set of FineDance@Dancer was divided by different dancers, which test set contains fewer dance genres, yet the same dancer won't appear in different sets.

If you use this dataset for dance generation, we recommend you to use the split of FineDance@Genre.

Acknowledgments

We would like to express our sincere gratitude to Dr Yan Zhang and Yulun Zhang for their invaluable guidance and insights during the course of our research.

This code is standing on the shoulders of giants. We want to thank the following contributors that our code is based on: EDGE,MDM,Adan,Diffusion,SMPLX.

Citation

When using the code/figures/data/video/etc., please cite our work

@inproceedings{li2023finedance,
  title={FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance Generation},
  author={Li, Ronghui and Zhao, Junfan and Zhang, Yachao and Su, Mingyang and Ren, Zeping and Zhang, Han and Tang, Yansong and Li, Xiu},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={10234--10243},
  year={2023}
}