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.

通过引入两种化妆先验模型(基于PCA和基于StyleGAN2的模型)，我们有效地解决了先前方法在鲁棒地估计化妆方面所面临的挑战，尤其是处理自遮挡面部的情况。实验中，我们证明了我们的方法在降低计算成本方面有数倍的优势，并提高了化妆估计的准确性，在3D面部化妆应用中具有广泛的优势，如3D化妆面部重建、用户友好的化妆编辑、化妆转移和插值。

3D面部化妆估计和应用的化妆前模型