Face beautification: Beyond makeup transfer

Abstract

Facial appearance plays an important role in our social lives. Subjective perception of women's beauty depends on various face-related (e.g., skin, shape, hair) and environmental (e.g., makeup, lighting, angle) factors. Similarly to cosmetic surgery in the physical world, virtual face beautification is an emerging field with many open issues to be addressed. Inspired by the latest advances in style-based synthesis and face beauty prediction, we propose a novel framework for face beautification. For a given reference face with a high beauty score, our GAN-based architecture is capable of translating an inquiry face into a sequence of beautified face images with the referenced beauty style and the target beauty score values. To achieve this objective, we propose to integrate both style-based beauty representation (extracted from the reference face) and beauty score prediction (trained on the SCUT-FBP database) into the beautification process. Unlike makeup transfer, our approach targets many-to-many (instead of one-to-one) translation, where multiple outputs can be defined by different references with various beauty scores. Extensive experimental results are reported to demonstrate the effectiveness and flexibility of the proposed face beautification framework. To support reproducible research, the source codes accompanying this work will be made publicly available on GitHub.