  Deep person generation has attracted extensive research attention due to its
wide applications in virtual agents, video conferencing, online shopping and
art/movie production. With the advancement of deep learning, visual appearances
(face, pose, cloth) of a person image can be easily generated or manipulated on
demand. In this survey, we first summarize the scope of person generation, and
then systematically review recent progress and technical trends in deep person
generation, covering three major tasks: talking-head generation (face),
pose-guided person generation (pose) and garment-oriented person generation
(cloth). More than two hundred papers are covered for a thorough overview, and
the milestone works are highlighted to witness the major technical
breakthrough. Based on these fundamental tasks, a number of applications are
investigated, e.g., virtual fitting, digital human, generative data
augmentation. We hope this survey could shed some light on the future prospects
of deep person generation, and provide a helpful foundation for full
applications towards digital human.
