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.

翻译：人像修图旨在提升输入人像照片的美学质量，尤其需要优先处理人体区域。基于深度学习的方法大幅提高了修图效率，并提供了令人满意的修图结果。然而，现有的人像修图方法侧重于自动修图，将所有人体区域同等对待，忽略了用户对具体个体的偏好，因此在交互场景中灵活性有限。本文强调用户意图的重要性，并探索了交互式人像修图任务。具体而言，我们提出了一种区域感知的修图框架，包括两个分支：自动分支和交互分支。自动分支涉及编码-解码过程，用于搜索候选区域并在无用户引导下执行自动区域感知修图。交互分支将稀疏的用户引导编码为优先级条件向量，并通过区域选择模块调制潜在特征，以进一步强调用户指定的区域。实验结果表明，我们的交互分支有效捕捉了用户意图，并能在稀疏用户引导下良好泛化至未见场景；同时，由于区域感知能力的提升，我们的自动分支也优于当前最先进的修图方法。