[CVPR2022] Thin-Plate Spline Motion Model for Image Animation

Source code of the CVPR'2022 paper "Thin-Plate Spline Motion Model for Image Animation"

Paper | Supp

Example animation

PS: The paper trains the model for 100 epochs for a fair comparison. You can use more data and train for more epochs to get better performance.

Web demo for animation

• Integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo:
• Try the web demo for animation here:
• Google Colab:

Pre-trained models

• Tsinghua Cloud
• Yandex
• Google Drive
• Baidu Yun

Installation

We support python3.(Recommended version is Python 3.9). To install the dependencies run:

pip install -r requirements.txt

YAML configs

There are several configuration files one for each dataset in the config folder named as config/dataset_name.yaml.

See description of the parameters in the config/taichi-256.yaml.

Datasets

1. MGif. Follow Monkey-Net.

2. TaiChiHD and VoxCeleb. Follow instructions from video-preprocessing.

3. TED-talks. Follow instructions from MRAA.

Here are VoxCeleb, TaiChiHD and TED-talks pre-processed datasets used in the paper. Baidu Yun Download all files under the folder, then merge the files and decompress, for example:

cat vox.tar.* > vox.tar
tar xvf vox.tar

Training

To train a model on specific dataset run:

CUDA_VISIBLE_DEVICES=0,1 python run.py --config config/dataset_name.yaml --device_ids 0,1

A log folder named after the timestamp will be created. Checkpoints, loss values, reconstruction results will be saved to this folder.

Training AVD network

To train a model on specific dataset run:

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

Checkpoints, loss values, reconstruction results will be saved to {checkpoint_folder}.

Evaluation on video reconstruction

To evaluate the reconstruction performance run:

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

The reconstruction subfolder will be created in {checkpoint_folder}. The generated video will be stored to this folder, also generated videos will be stored in png subfolder in loss-less '.png' format for evaluation. To compute metrics, follow instructions from pose-evaluation.

Image animation demo

• notebook: demo.ipynb, edit the config cell and run for image animation.
• python:

CUDA_VISIBLE_DEVICES=0 python demo.py --config config/vox-256.yaml --checkpoint checkpoints/vox.pth.tar --source_image ./source.jpg --driving_video ./driving.mp4

Acknowledgments

The main code is based upon FOMM and MRAA

Thanks for the excellent works!

And Thanks to:

• @chenxwh: Add Web Demo & Docker environment

• @TalkUHulk: The C++/Python demo is provided in Image-Animation-Turbo-Boost

• @AK391: Add huggingface web demo