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Adaptive Super Resolution For One-Shot Talking-Head Generation
The repository for ICASSP2024 Adaptive Super Resolution For One-Shot Talking-Head Generation (AdaSR TalkingHead)
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/
Updates
- [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.
Installation
Clone this repo:
git clone git@github.com:Songluchuan/AdaSR-TalkingHead.git
cd AdaSR-TalkingHead
Dependencies:
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
Inference Code
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/
The demo video and reference image are under
DEMO/
The inference code is in the
run_demo.sh
, please run it with
bash run_demo.sh
- 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
Video
Citation
@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, ESRGAN and unofficial face2vid. Thanks to the authors contribution.
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