Survey of Face Synthesis

Abstract

Face synthesis is one of the hot topics in the field of computer vision because of its application and technical value. In recent years, the breakthrough of deep learning has attracted much attention in this field. This paper divides the research in this field into four subcategories: face identity synthesis, face movements synthesis, face attributes synthesis and face generation, and systematically summarizes the development process, status quo, and existing problems of these subcategories. First of all, for face identity synthesis, three approaches are summarized, including computer graphics, digital image processing and deep learning. This paper summarizes their respective routine processes, and analyzes the technical principles of milestone work in detail. Secondly, face movements synthesis is further divided into label driven expression editing and real face driven face reenactment, where the shortcomings and problems in each field are pointed out. Then, the development of face attribute synthesis based on generative model is introduced, especially generative adversarial network. Finally, this paper briefly describes all kinds of researches on face generation. In addition, this paper also introduces the practical application and related problems of face synthesis field and provides the possible research direction in this field.

Keywords

• face synthesis
• generative adversarial network (gan)
• deep learning