Portrait retouching aims to improve the aesthetic quality of input portrait
photos and especially requires human-region priority. The deep learning-based
methods largely elevate the retouching efficiency and provide promising
retouched results. However, existing portrait retouching methods focus on
automatic retouching, which treats all human-regions equally and ignores users'
preferences for specific individuals, thus suffering from limited flexibility
in interactive scenarios. In this work, we emphasize the importance of users'
intents and explore the interactive portrait retouching task. Specifically, we
propose a region-aware retouching framework with two branches: an automatic
branch and an interactive branch. The automatic branch involves an
encoding-decoding process, which searches region candidates and performs
automatic region-aware retouching without user guidance. The interactive branch
encodes sparse user guidance into a priority condition vector and modulates
latent features with a region selection module to further emphasize the
user-specified regions. Experimental results show that our interactive branch
effectively captures users' intents and generalizes well to unseen scenes with
sparse user guidance, while our automatic branch also outperforms the
state-of-the-art retouching methods due to improved region-awareness.

本文提出一种基于深度学习的面部照片交互修饰框架，旨在提高用户界别优先级，包括自动分支和交互分支，后者通过将用户指定区域编码进优先级条件向量和调制地潜在特征来进一步强调用户指定区域，实验证明该方法能够有效地捕捉用户意图且比现有方法更具区域感知性和灵活性。