Recent studies on face attribute transfer have achieved great success. A lot
of models are able to transfer face attributes with an input image. However,
they suffer from three limitations: (1) incapability of generating image by
exemplars; (2) being unable to transfer multiple face at
本文提出一种基于 GAN 模型的高分辨率人脸交换方法,通过显式地将潜在空间的语义进行分离,并通过引入基于地标的结构转换潜在方向来分离结构属性中的身份和姿态信息,进而获得丰富的生成特征。通过加入空间 - 时间约束,将这种方法进一步扩展到视频人脸交换,并在实验证明,该方法在幻觉质量和连续性方面优于现有方法。