Adaptive Super Resolution For One-Shot Talking-Head Generation
Luchuan Song Pinxin Liu Guojun Yin Chenliang Xu
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/}.