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.

翻译：创作精细修饰的人像图像即使对于专业艺术家而言也是一项繁琐且耗时的任务。现有自动修饰方法或存在过度平滑伪影，或缺乏泛化能力。为解决此问题，我们提出StyleRetoucher——一种利用StyleGAN的生成与泛化能力改善输入人像皮肤状况同时保留面部细节的新型自动人像修饰框架。借助预训练StyleGAN的先验知识，我们的方法展现出卓越鲁棒性：a) 在较少训练样本下稳定运行，b) 对域外数据具有良好泛化能力。此外，通过融合输入图像的空间特征与StyleGAN各层的中间特征，本方法最大程度保留了输入特征。我们进一步提出一种新颖的瑕疵感知特征选择机制，可有效识别并去除皮肤瑕疵，改善图像皮肤状态。定性与定量评估验证了本方法的强大泛化能力。后续实验表明，StyleRetoucher在图像修饰任务中优于其他备选方案。我们同时开展用户感知研究，证实本方法的修饰性能超越现有最先进替代方案。