Adaptive Super Resolution For One-Shot Talking-Head Generation
<|reference_start|>Adaptive Super Resolution For One-Shot Talking-Head Generation: 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: \url{https://github.com/Songluchuan/AdaSR-TalkingHead/}.<|reference_end|>
@article{song2024adaptive,
    title={Adaptive Super Resolution For One-Shot Talking-Head Generation},
    author={Luchuan Song, Pinxin Liu, Guojun Yin, Chenliang Xu},
    journal={arXiv preprint arXiv:2403.15944},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15944},
    primaryClass={cs.CV cs.AI eess.IV}
}