StyleRetoucher: Generalized Portrait Image Retouching with GAN Priors
Wanchao Su Can Wang Chen Liu Hangzhou Han Hongbo Fu Jing Liao
Creating fine-retouched portrait images is tedious and time-consuming even
for professional artists. There exist automatic retouching methods, but they
either suffer from over-smoothing artifacts or lack generalization ability. To
address such issues, we present StyleRetoucher, a novel automatic portrait
image retouching framework, leveraging StyleGAN's generation and generalization
ability to improve an input portrait image's skin condition while preserving
its facial details. Harnessing the priors of pretrained StyleGAN, our method
shows superior robustness: a). performing stably with fewer training samples
and b). generalizing well on the out-domain data. Moreover, by blending the
spatial features of the input image and intermediate features of the StyleGAN
layers, our method preserves the input characteristics to the largest extent.
We further propose a novel blemish-aware feature selection mechanism to
effectively identify and remove the skin blemishes, improving the image skin
condition. Qualitative and quantitative evaluations validate the great
generalization capability of our method. Further experiments show
StyleRetoucher's superior performance to the alternative solutions in the image
retouching task. We also conduct a user perceptive study to confirm the
superior retouching performance of our method over the existing
state-of-the-art alternatives.