Facial expression editing has attracted increasing attention with the advance of deep neural networks in recent years. However, most existing methods suffer from compromised editing fidelity and limited usability as they either ignore pose variations (unrealistic editing) or require paired training data (not easy to collect) for pose controls. This paper presents POCE, an innovative pose-controllable expression editing network that can generate realistic facial expressions and head poses simultaneously with just unpaired training images. POCE achieves the more accessible and realistic pose-controllable expression editing by mapping face images into UV space, where facial expressions and head poses can be disentangled and edited separately. POCE has two novel designs. The first is self-supervised UV completion that allows to complete UV maps sampled under different head poses, which often suffer from self-occlusions and missing facial texture. The second is weakly-supervised UV editing that allows to generate new facial expressions with minimal modification of facial identity, where the synthesized expression could be controlled by either an expression label or directly transplanted from a reference UV map via feature transfer. Extensive experiments show that POCE can learn from unpaired face images effectively, and the learned model can generate realistic and high-fidelity facial expressions under various new poses.

翻译：近年来，随着深度神经网络的进步，面部表情编辑引起了越来越多的关注。然而，现有方法大多存在编辑保真度受损和可用性有限的问题，因为它们要么忽略姿态变化（导致编辑不真实），要么需要成对的训练数据（不易收集）来实现姿态控制。本文提出了POCE，一种创新的姿态可控表情编辑网络，仅需非成对训练图像即可同时生成真实的面部表情和头部姿态。POCE通过将人脸图像映射到UV空间，实现了更易获取且更真实的姿态可控表情编辑，在该空间中面部表情和头部姿态可以解耦并分别编辑。POCE包含两个新颖设计：其一是自监督的UV补全，它能补全在不同头部姿态下采样时往往存在自遮挡和面部纹理缺失的UV图；其二是弱监督的UV编辑，它能在最小化面部身份信息修改的情况下生成新的面部表情，其中合成的表情可通过表情标签控制，或通过特征迁移直接从参考UV图中移植。大量实验表明，POCE能够从非成对的人脸图像中有效学习，且学习到的模型能生成各种新姿态下真实且高保真的面部表情。