Hexiang Wang, Fengqi Liu, Qianyu Zhou, Ran Yi, Xin Tan, and Lizhuang Ma

Image animation aims to bring static images to life according to driving videos and create engaging visual content that can be used for various purposes such as animation, entertainment, and education. Recent unsupervised methods utilize affine and thin-plate spline transformations based on keypoints to transfer the motion in driving frames to the source image. However, limited by the expressive power of the transformations used, these methods always produce poor results when the gap between the motion in the driving frame and the source image is large. To address this issue, we propose to model motion from the source image to the driving frame in highly-expressive diffeomorphism spaces. Firstly, we introduce Continuous Piecewise-Affine based (CPAB) transformation to model the motion and present a well-designed inference algorithm to generate CPAB transformation from control keypoints. Secondly, we propose a SAM-guided keypoint semantic loss to further constrain the keypoint extraction process and improve the semantic consistency between the corresponding keypoints on the source and driving images. Finally, we design a structure alignment loss to align the structure-related features extracted from driving and generated images, thus helping the generator generate results that are more consistent with the driving action. Extensive experiments on four datasets demonstrate the effectiveness of our method against state-of-the-art competitors quantitatively and qualitatively.

This work has been accepted by AAAI 2024.

Clone this project in recursive mode:

git clone --recursive https://github.com/DevilPG/AAAI2024-CPABMM.git

For detailed information about the CPAB transformation library, please refer to libcpab.

Please run the project in python3 environment. To install the dependencies run:

pip install -r requirements.txt

Configuration files are in the config folder, one for each dataset, named as config/dataset_name.yaml.

For detailed information of four datasets used in this paper, please follow TPSMM.

To train a model on specific dataset run:

python run.py --config config/dataset_name.yaml --log_dir dir_to_save_log_files --tag id_for_training_task

To train a model on specific dataset run:

python run.py --mode train_avd --checkpoint '{checkpoint_folder}/checkpoint.pth.tar' --config config/dataset_name.yaml

To evaluate the model on video reconstruction task run:

CUDA_VISIBLE_DEVICES=0 python run.py --mode reconstruction --config config/dataset_name.yaml --checkpoint '{checkpoint_folder}/checkpoint.pth.tar'

To compute quantitative metrics, please follow instructions from pose-evaluation.

To generate a animation video with a source image and a driving video run:

python demo.py --config config/dataset_name.yaml --checkpoint {checkpoint_folder}/checkpoint.pth.tar --source_image path_to_source_img --driving_video path_to_driving_video

If you find this code helpful for your research, please cite:

@article{wang2024cpabmm,
  title={Continuous Piecewise-Affine Based Motion Model for Image Animation},
  author={Hexiang Wang and Fengqi Liu and Qianyu Zhou and Ran Yi and Xin Tan and Lizhuang Ma},
  journal={arXiv preprint arXiv:2401.09146},
  year={2024}
}