BeautyGRPO: Aesthetic Alignment for Face Retouching via Dynamic Path Guidance and Fine-Grained Preference Modeling
Jiachen Yang, Xianhui Lin, Yi Dong, Zebiao Zheng, Xing Liu, Hong Gu, Yanmei Fang

TL;DR
BeautyGRPO is a reinforcement learning framework that improves face retouching by aligning with human aesthetic preferences through a novel dynamic path guidance method and a fine-grained preference dataset.
Contribution
The paper introduces a new RL-based face retouching method with dynamic path guidance and a fine-grained preference dataset, addressing fidelity and preference alignment challenges.
Findings
Outperforms existing face retouching methods in aesthetic quality
Achieves better texture detail and blemish removal
Aligns more closely with human aesthetic preferences
Abstract
Face retouching requires removing subtle imperfections while preserving unique facial identity features, in order to enhance overall aesthetic appeal. However, existing methods suffer from a fundamental trade-off. Supervised learning on labeled data is constrained to pixel-level label mimicry, failing to capture complex subjective human aesthetic preferences. Conversely, while online reinforcement learning (RL) excels at preference alignment, its stochastic exploration paradigm conflicts with the high-fidelity demands of face retouching and often introduces noticeable noise artifacts due to accumulated stochastic drift. To address these limitations, we propose BeautyGRPO, a reinforcement learning framework that aligns face retouching with human aesthetic preferences. We construct FRPref-10K, a fine-grained preference dataset covering five key retouching dimensions, and train a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVisual Attention and Saliency Detection · Face recognition and analysis · Generative Adversarial Networks and Image Synthesis
