Makeup Prior Models for 3D Facial Makeup Estimation and Applications
Xingchao Yang Takafumi Taketomi Yuki Endo Yoshihiro Kanamori
In this work, we introduce two types of makeup prior models to extend
existing 3D face prior models: PCA-based and StyleGAN2-based priors. The
PCA-based prior model is a linear model that is easy to construct and is
computationally efficient. However, it retains only low-frequency information.
Conversely, the StyleGAN2-based model can represent high-frequency information
with relatively higher computational cost than the PCA-based model. Although
there is a trade-off between the two models, both are applicable to 3D facial
makeup estimation and related applications. By leveraging makeup prior models
and designing a makeup consistency module, we effectively address the
challenges that previous methods faced in robustly estimating makeup,
particularly in the context of handling self-occluded faces. In experiments, we
demonstrate that our approach reduces computational costs by several orders of
magnitude, achieving speeds up to 180 times faster. In addition, by improving
the accuracy of the estimated makeup, we confirm that our methods are highly
advantageous for various 3D facial makeup applications such as 3D makeup face
reconstruction, user-friendly makeup editing, makeup transfer, and
interpolation.