| # Adaptive Super Resolution For One-Shot Talking-Head Generation |
| The repository for ICASSP2024 Adaptive Super Resolution For One-Shot Talking-Head Generation (AdaSR TalkingHead) |
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| ## Abstract |
| The one-shot talking-head generation learns to synthesize a talking-head video with one source portrait image under the driving of same or different identity video. Usually these methods require plane-based pixel transformations via Jacobin matrices or facial image warps for novel poses generation. The constraints of using a single image source and pixel displacements often compromise the clarity of the synthesized images. Some methods try to improve the quality of synthesized videos by introducing additional super-resolution modules, but this will undoubtedly increase computational consumption and destroy the original data distribution. In this work, we propose an adaptive high-quality talking-head video generation method, which synthesizes high-resolution video without additional pre-trained modules. Specifically, inspired by existing super-resolution methods, we down-sample the one-shot source image, and then adaptively reconstruct high-frequency details via an encoder-decoder module, resulting in enhanced video clarity. Our method consistently improves the quality of generated videos through a straightforward yet effective strategy, substantiated by quantitative and qualitative evaluations. The code and demo video are available on: https://github.com/Songluchuan/AdaSR-TalkingHead/ |
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| ## Updates |
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| - [03/2024] Inference code and pretrained model are released. |
| - [03/2024] Arxiv Link: https://arxiv.org/abs/2403.15944. |
| - [COMING] Super-resolution model (based on StyleGANEX and ESRGAN). |
| - [COMING] Train code and processed datasets. |
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| ## Installation |
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| **Clone this repo:** |
| ```bash |
| git clone git@github.com:Songluchuan/AdaSR-TalkingHead.git |
| cd AdaSR-TalkingHead |
| ``` |
| **Dependencies:** |
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| We have tested on: |
| - CUDA 11.3-11.6 |
| - PyTorch 1.10.1 |
| - Matplotlib 3.4.3; Matplotlib 3.4.2; opencv-python 4.7.0; scikit-learn 1.0; tqdm 4.62.3 |
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| ## Inference Code |
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| 1. Download the pretrained model on google drive: https://drive.google.com/file/d/1g58uuAyZFdny9_twvbv0AHxB9-03koko/view?usp=sharing (it is trained on the HDTF dataset), and put it under checkpoints/<br> |
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| 2. The demo video and reference image are under ```DEMO/``` |
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| 3. The inference code is in the ```run_demo.sh```, please run it with |
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| ``` |
| bash run_demo.sh |
| ``` |
| |
| 4. You can set different demo image and driven video in the ```run_demo.sh``` |
| ``` |
| --source_image DEMO/demo_img_3.jpg |
| ``` |
| and |
| ``` |
| --driving_video DEMO/demo_video_1.mp4 |
| ``` |
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| ## Video |
| <div align="center"> |
| <a href="https://www.youtube.com/watch?v=B_-3F51QmKE" target="_blank"> |
| <img src="media/Teaser_video.png" alt="AdaSR Talking-Head" width="1120" style="height: auto;" /> |
| </a> |
| </div> |
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| ## Citation |
| |
| ```bibtex |
| @inproceedings{song2024adaptive, |
| title={Adaptive Super Resolution for One-Shot Talking Head Generation}, |
| author={Song, Luchuan and Liu, Pinxin and Yin, Guojun and Xu, Chenliang}, |
| year={2024}, |
| organization={IEEE International Conference on Acoustics, Speech, and Signal Processing} |
| } |
| ``` |
| |
| ## Acknowledgments |
| |
| The code is mainly developed based on [styleGANEX](https://github.com/williamyang1991/StyleGANEX), [ESRGAN](https://github.com/xinntao/ESRGAN) and [unofficial face2vid](https://github.com/zhanglonghao1992/One-Shot_Free-View_Neural_Talking_Head_Synthesis). Thanks to the authors contribution. |
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