Spaces:
Runtime error
Runtime error
File size: 6,881 Bytes
30f37fa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 |
---
license: mit
---
<h1 align="center">LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control</h1>
<div align='center'>
<a href='https://github.com/cleardusk' target='_blank'><strong>Jianzhu Guo</strong></a><sup> 1β </sup> 
<a href='https://github.com/KwaiVGI' target='_blank'><strong>Dingyun Zhang</strong></a><sup> 1,2</sup> 
<a href='https://github.com/KwaiVGI' target='_blank'><strong>Xiaoqiang Liu</strong></a><sup> 1</sup> 
<a href='https://github.com/KwaiVGI' target='_blank'><strong>Zhizhou Zhong</strong></a><sup> 1,3</sup> 
<a href='https://scholar.google.com.hk/citations?user=_8k1ubAAAAAJ' target='_blank'><strong>Yuan Zhang</strong></a><sup> 1</sup> 
</div>
<div align='center'>
<a href='https://scholar.google.com/citations?user=P6MraaYAAAAJ' target='_blank'><strong>Pengfei Wan</strong></a><sup> 1</sup> 
<a href='https://openreview.net/profile?id=~Di_ZHANG3' target='_blank'><strong>Di Zhang</strong></a><sup> 1</sup> 
</div>
<div align='center'>
<sup>1 </sup>Kuaishou Technology  <sup>2 </sup>University of Science and Technology of China  <sup>3 </sup>Fudan University 
</div>
<br>
<div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;">
<!-- <a href='LICENSE'><img src='https://img.shields.io/badge/license-MIT-yellow'></a> -->
<a href='https://arxiv.org/pdf/2407.03168'><img src='https://img.shields.io/badge/arXiv-LivePortrait-red'></a>
<a href='https://liveportrait.github.io'><img src='https://img.shields.io/badge/Project-LivePortrait-green'></a>
<a href='https://huggingface.co/spaces/KwaiVGI/liveportrait'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a>
</div>
<br>
<p align="center">
<img src="./docs/showcase2.gif" alt="showcase">
<br>
π₯ For more results, visit our <a href="https://liveportrait.github.io/"><strong>homepage</strong></a> π₯
</p>
## π₯ Updates
- **`2024/07/04`**: π₯ We released the initial version of the inference code and models. Continuous updates, stay tuned!
- **`2024/07/04`**: π We released the [homepage](https://liveportrait.github.io) and technical report on [arXiv](https://arxiv.org/pdf/2407.03168).
## Introduction
This repo, named **LivePortrait**, contains the official PyTorch implementation of our paper [LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control](https://arxiv.org/pdf/2407.03168).
We are actively updating and improving this repository. If you find any bugs or have suggestions, welcome to raise issues or submit pull requests (PR) π.
## π₯ Getting Started
### 1. Clone the code and prepare the environment
```bash
git clone https://github.com/KwaiVGI/LivePortrait
cd LivePortrait
# create env using conda
conda create -n LivePortrait python==3.9.18
conda activate LivePortrait
# install dependencies with pip
pip install -r requirements.txt
```
### 2. Download pretrained weights
Download our pretrained LivePortrait weights and face detection models of InsightFace from [Google Drive](https://drive.google.com/drive/folders/1UtKgzKjFAOmZkhNK-OYT0caJ_w2XAnib) or [Baidu Yun](https://pan.baidu.com/s/1MGctWmNla_vZxDbEp2Dtzw?pwd=z5cn). We have packed all weights in one directory π. Unzip and place them in `./pretrained_weights` ensuring the directory structure is as follows:
```text
pretrained_weights
βββ insightface
β βββ models
β βββ buffalo_l
β βββ 2d106det.onnx
β βββ det_10g.onnx
βββ liveportrait
βββ base_models
β βββ appearance_feature_extractor.pth
β βββ motion_extractor.pth
β βββ spade_generator.pth
β βββ warping_module.pth
βββ landmark.onnx
βββ retargeting_models
βββ stitching_retargeting_module.pth
```
### 3. Inference π
```bash
python inference.py
```
If the script runs successfully, you will get an output mp4 file named `animations/s6--d0_concat.mp4`. This file includes the following results: driving video, input image, and generated result.
<p align="center">
<img src="./docs/inference.gif" alt="image">
</p>
Or, you can change the input by specifying the `-s` and `-d` arguments:
```bash
python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4
# or disable pasting back
python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4 --no_flag_pasteback
# more options to see
python inference.py -h
```
**More interesting results can be found in our [Homepage](https://liveportrait.github.io)** π
### 4. Gradio interface
We also provide a Gradio interface for a better experience, just run by:
```bash
python app.py
```
### 5. Inference speed evaluation πππ
We have also provided a script to evaluate the inference speed of each module:
```bash
python speed.py
```
Below are the results of inferring one frame on an RTX 4090 GPU using the native PyTorch framework with `torch.compile`:
| Model | Parameters(M) | Model Size(MB) | Inference(ms) |
|-----------------------------------|:-------------:|:--------------:|:-------------:|
| Appearance Feature Extractor | 0.84 | 3.3 | 0.82 |
| Motion Extractor | 28.12 | 108 | 0.84 |
| Spade Generator | 55.37 | 212 | 7.59 |
| Warping Module | 45.53 | 174 | 5.21 |
| Stitching and Retargeting Modules| 0.23 | 2.3 | 0.31 |
*Note: the listed values of Stitching and Retargeting Modules represent the combined parameter counts and the total sequential inference time of three MLP networks.*
## Acknowledgements
We would like to thank the contributors of [FOMM](https://github.com/AliaksandrSiarohin/first-order-model), [Open Facevid2vid](https://github.com/zhanglonghao1992/One-Shot_Free-View_Neural_Talking_Head_Synthesis), [SPADE](https://github.com/NVlabs/SPADE), [InsightFace](https://github.com/deepinsight/insightface) repositories, for their open research and contributions.
## Citation π
If you find LivePortrait useful for your research, welcome to π this repo and cite our work using the following BibTeX:
```bibtex
@article{guo2024live,
title = {LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control},
author = {Jianzhu Guo and Dingyun Zhang and Xiaoqiang Liu and Zhizhou Zhong and Yuan Zhang and Pengfei Wan and Di Zhang},
year = {2024},
journal = {arXiv preprint:2407.03168},
}
```
|