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.

翻译：深度人物生成因其在虚拟代理、视频会议、在线购物以及影视制作中的广泛应用而备受研究关注。随着深度学习的进展，人物图像的可视外观（人脸、姿态、衣物）可按需轻松生成或操控。本综述首先界定人物生成的范畴，继而系统梳理深度人物生成领域的最新进展与技术趋势，涵盖三大核心任务：说话头像生成（人脸）、姿态引导人物生成（姿态）与面向衣物的人物生成（衣物）。为提供全面概述，本文涵盖两百余篇论文，并重点标注里程碑式工作以见证关键技术突破。基于这些基础任务，本文探讨了诸多应用场景，如虚拟试衣、数字人、生成式数据增强等。我们期望本综述能为深度人物生成的未来前景提供启示，并为迈向数字人的完整应用奠定有益基础。