ORIGINAL_README.md

X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention

You Xie, Hongyi Xu, Guoxian Song, Chao Wang, Yichun Shi, Linjie Luo
  ByteDance Inc.

This repository contains the video generation code of SIGGRAPH 2024 paper X-Portrait.

Installation

Note: Python 3.9 and Cuda 11.8 are required.

bash env_install.sh

Model

Please download pre-trained model from here, and save it under "checkpoint/"

Testing

bash scripts/test_xportrait.sh

parameters:
model_config: config file of the corresponding model
output_dir: output path for generated video
source_image: path of source image
driving_video: path of driving video
best_frame: specify the frame index in the driving video where the head pose best matches the source image (note: precision of best_frame index might affect the final quality)
out_frames: number of generation frames
num_mix: number of overlapping frames when applying prompt travelling during inference
ddim_steps: number of inference steps (e.g., 30 steps for ddim)

Performance Boost

efficiency: Our model is compatible with LCM LoRA (https://huggingface.co/latent-consistency/lcm-lora-sdv1-5), which helps reduce the number of inference steps.
expressiveness: Expressiveness of the results could be boosted if results of other face reenactment approaches, e.g., face vid2vid, could be provided via parameter "--initial_facevid2vid_results".

🎓 Citation

If you find this codebase useful for your research, please use the following entry.

@inproceedings{xie2024x,
  title={X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention},
  author={Xie, You and Xu, Hongyi and Song, Guoxian and Wang, Chao and Shi, Yichun and Luo, Linjie},
  journal={arXiv preprint arXiv:2403.15931},
  year={2024}
}