We propose a method to disentangle linear-encoded facial semantics from StyleGAN without external supervision. The method derives from linear regression and sparse representation learning concepts to make the disentangled latent representations easily interpreted as well. We start by coupling StyleGAN with a stabilized 3D deformable facial reconstruction method to decompose single-view GAN generations into multiple semantics. Latent representations are then extracted to capture interpretable facial semantics. In this work, we make it possible to get rid of labels for disentangling meaningful facial semantics. Also, we demonstrate that the guided extrapolation along the disentangled representations can help with data augmentation, which sheds light on handling unbalanced data. Finally, we provide an analysis of our learned localized facial representations and illustrate that the semantic information is encoded, which surprisingly complies with human intuition. The overall unsupervised design brings more flexibility to representation learning in the wild.

本文介绍了一种无需外部监督即可将线性编码的面部语义从StyleGAN中分离的方法，并结合稳定的三维可变形面部重建方法将单视图GAN生成物分解为多个语义。同时，我们还展示了沿着分离表示进行引导外推可以帮助进行数据增强，并提供了对我们学习到的面部表示的分析。

线性编码面部语义的无监督解缠