Deep Person Generation: A Survey from the Perspective of Face, Pose, and Cloth Synthesis

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Abstract
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 on demand. In this survey, we first summarize the scope of person generation, and then systematically review recent progress and technical trends in identity-preserving 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, many applications are investigated, e.g., virtual fitting, digital human, and generative data augmentation. We hope this survey could shed some light on the future prospects of identity-preserving deep person generation, and provide a helpful foundation for full applications towards the digital human.
Funding Information
  • National Natural Science Foundation of China (62276017, U1636211, 61672081)
  • 2022 Tencent Big Travel Rhino-Bird Special Research Program
  • Fund of the State Key Laboratory of Software Development Environment (SKLSDE-2021ZX-18)

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