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https://github.com/bytedance/x-portrait

Source code for the SIGGRAPH 2024 paper "X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention"

https://github.com/bytedance/x-portrait

Science Score: 23.0%

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    Low similarity (11.3%) to scientific vocabulary

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research
Last synced: 11 months ago · JSON representation

Repository

Source code for the SIGGRAPH 2024 paper "X-Portrait: Expressive Portrait Animation with Hierarchical Motion Attention"

Basic Info
  • Host: GitHub
  • Owner: bytedance
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 9.1 MB
Statistics
  • Stars: 522
  • Watchers: 15
  • Forks: 44
  • Open Issues: 12
  • Releases: 0
Topics
research
Created about 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License

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.

Paper PDF Project Page Youtube

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

Installation

Note: Python 3.9 and Cuda 11.8 are required. shell bash env_install.sh

Model

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

Testing

shell 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 bestframe index might affect the final quality)
**outframes: 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 "--initialfacevid2vidresults".

🎓 Citation

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

Owner

  • Name: Bytedance Inc.
  • Login: bytedance
  • Kind: organization
  • Location: Singapore

GitHub Events

Total
  • Issues event: 5
  • Watch event: 418
  • Issue comment event: 17
  • Fork event: 38
Last Year
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Committers

Last synced: about 1 year ago

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  • Total Committers: 1
  • Avg Commits per committer: 10.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 10
  • Committers: 1
  • Avg Commits per committer: 10.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
you.xie y****e@b****m 10
Committer Domains (Top 20 + Academic)
bytedance.com: 1

Issues and Pull Requests

Last synced: about 1 year ago

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Past Year
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  • Pull requests: 0
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  • Average time to close pull requests: N/A
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  • Pull request authors: 0
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Dependencies

requirements.txt pypi
  • accelerate ==0.17.0
  • black ==23.7.0
  • byted-dataloader ==0.3.7
  • byted-mloops ==0.2.21
  • chardet ==5.1.0
  • decord *
  • diffusers ==0.26.0
  • einops >=0.6.1
  • einops_exts ==0.0.4
  • ema-pytorch ==0.2.1
  • entmax ==1.1
  • fairscale >=0.4.13
  • fire >=0.5.0
  • fsspec >=2023.6.0
  • ftfy ==6.1.1
  • imageio ==2.9.0
  • imageio-ffmpeg ==0.4.2
  • invisible-watermark >=0.2.0
  • kornia ==0.6.11
  • matplotlib >=3.7.2
  • natsort >=8.4.0
  • ninja >=1.11.1
  • numpy >=1.24.4
  • omegaconf >=2.3.0
  • open-clip-torch >=2.20.0
  • opencv-python ==4.7.0.72
  • pandas >=2.0.3
  • pillow *
  • pudb >=2022.1.3
  • pytorch-lightning ==1.4.2
  • pyyaml >=5.4.1
  • regex ==2022.10.31
  • rotary_embedding_torch ==0.2.1
  • scikit-image ==0.19.3
  • scipy >=1.10.1
  • streamlit >=0.73.1
  • streamlit-keyup ==0.2.0
  • tensorboard ==2.11.2
  • tensorboardx ==2.6
  • thriftpy2 *
  • timm >=0.9.2
  • tokenizers ==0.12.1
  • torch >=2.0.1
  • torchaudio >=2.0.2
  • torchdata ==0.6.1
  • torchdiffeq ==0.2.3
  • torchmetrics ==0.6.0
  • torchvision >=0.15.2
  • tqdm >=4.65.0
  • transformers ==4.30.0
  • triton ==2.0.0
  • urllib3 <1.27,>=1.25.4
  • wandb >=0.15.6
  • webdataset >=0.2.33
  • wheel >=0.41.0
  • xformers >=0.0.22