  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. Similar
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 of 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 referenced beauty style and
targeted 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 SCUT-FBP database) into the
process of beautification. Unlike makeup transfer, our approach targets at
many-to-many (instead of one-to-one) translation where multiple outputs can be
defined by either different references or varying beauty scores. Extensive
experimental results are reported to demonstrate the effectiveness and
flexibility of the proposed face beautification framework.
