Makeup Prior Models for 3D Facial Makeup Estimation and Applications
This work addresses challenges in 3D facial makeup applications like reconstruction and editing, offering incremental improvements in efficiency and robustness.
The paper tackles the problem of robust 3D facial makeup estimation, particularly for self-occluded faces, by introducing PCA-based and StyleGAN2-based makeup prior models and a makeup consistency module, resulting in computational speed-ups of up to 180 times faster and improved accuracy.
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.