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RealisDance: Equip controllable character animation with realistic hands

This repository is the official implementation of RealisDance. RealisDance enhances pose control of existing controllable character animation methods, achieving robust generation, smooth motion, and realistic hand quality. The architecture of RealisDance is shown as follows:

News

  • 2024-09-10: Now you can try more interesting AI video editing in XunGuang !!!
  • 2024-09-09: You may also be interested in our human part repair method RealisHuman.

Gallery

Here are several finger dances generated by RealisDance.

Quick Start

1. Setup Repository and Environment

git clone https://github.com/theFoxofSky/RealisDance.git
cd RealisDance

pip install -r requirements.txt
pip install -e .

2. Prepare Pretrained Checkpoints

mkdir pretrained_models
cd pretrained_models

# prepare rv-5-1 from https://huggingface.co/SG161222/Realistic_Vision_V5.1_noVAE/tree/main
mkdir RV
cd RV
ln -s $PATH-TO-RV-5-1 rv-5-1
cd ../

# prepare dino v2 from https://huggingface.co/facebook/dinov2-large/tree/main
mkdir DINO
cd DINO
ln -s $PATH-TO-DINOV2 dinov2
cd ../

# prepare motion module from https://github.com/guoyww/AnimateDiff
mkdir MM
cd MM
ln -s $PATH-TO-MM mm_v2.ckpt
cd ../

(Optional)
# prepare sd-vae-ft-mse from https://huggingface.co/stabilityai/sd-vae-ft-mse
# link to a SD dir with a subfolder named 'sd-vae-ft-mse'

3. Quick Inference

  • Download our trained RealisDance chekcpoint.

  • Prepare your reference image (a half-body selfie with a clean background will get better results).

  • Inference with demo sequences

python inference.py --config configs/stage2_hamer.yaml --smpl __assets__/demo_seq/smpl_1.mp4 \
    --hamer __assets__/demo_seq/hamer_1.mp4 --dwpose __assets__/demo_seq/dwpose_1.pkl \
    --ckpt $PATH-TO-CKPT --ref $PATH-TO-REF-IMG --output $PATH-TO-OUTPUT

4. Prepare Datasets

The structure of our dataset is highly customized, so please refer to our code to REWRITE the dataset part.

Note that the dataset returns a Dict like this:

{
    "data_key": id_of_data,
    "image": target_image_or_video,  # video should be in shape (B, C, F, H, W)
    "pose": target_dwpose_image_or_video,  # video should be in shape (B, C, F, H, W)
    "hamer": target_hamer_image_or_video,  # video should be in shape (B, C, F, H, W)
    "smpl": target_smpl_image_or_video,  # video should be in shape (B, C, F, H, W)
    "ref_image": reference_image, 
    "ref_image_clip": reference_dino_image,
}

5. Train Your RealisDance

# stage1
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --nnodes=1 --nproc_per_node=8 \
    train.py --config configs/stage1_hamer.yaml 

# stage2
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --nnodes=1 --nproc_per_node=8 \
    train.py --config configs/stage2_hamer.yaml

6. Evaluate Your RealisDance

# stage1
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --nnodes=1 --nproc_per_node=8 \
    evaluate.py --config configs/stage1_hamer.yaml --output $PATH-TO-OUTPUT --ckpt $PATH-TO-CKPT

# stage2
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --nnodes=1 --nproc_per_node=8 \
    evaluate.py --config configs/stage2_hamer.yaml --output $PATH-TO-OUTPUT --ckpt $PATH-TO-CKPT

Disclaimer

This project is released for academic use. We disclaim responsibility for user-generated content.

Contact Us

Jingkai Zhou: [email protected]

BibTeX

@article{zhou2024realisdance,
  title={RealisDance: Equip controllable character animation with realistic hands},
  author={Zhou, Jingkai and Wang, Benzhi and Chen, Weihua and Bai, Jingqi and Li, Dongyang and Zhang, Aixi and Xu, Hao and Yang, Mingyang and Wang, Fan},
  journal={arXiv preprint arXiv:2409.06202},
  year={2024}
}

Acknowledgements

Codebase built upon Open-Animate Anyone, Moore-Animate Anyone, and MusePose.