Image and video synthesis has become a blooming topic in computer vision and machine learning communities along with the developments of deep generative models, due to its great academic and application value. Many researchers have been devoted to synthesizing high-fidelity human images as one of the most commonly seen object categories in daily lives, where a large number of studies are performed based on various models, task settings and applications. Thus, it is necessary to give a comprehensive overview on these variant methods on human image generation. In this paper, we divide human image generation techniques into three paradigms, i.e., data-driven methods, knowledge-guided methods and hybrid methods. For each paradigm, the most representative models and the corresponding variants are presented, where the advantages and characteristics of different methods are summarized in terms of model architectures. Besides, the main public human image datasets and evaluation metrics in the literature are summarized. Furthermore, due to the wide application potentials, the typical downstream usages of synthesized human images are covered. Finally, the challenges and potential opportunities of human image generation are discussed to shed light on future research.

翻译：图像与视频合成已成为计算机视觉和机器学习领域中的热门课题，随着深度生成模型的发展，因其重要的学术与应用价值而备受关注。作为日常生活中最常见的物体类别之一，许多研究者致力于合成高保真人体图像，并基于不同模型、任务设定及应用场景开展了大量研究。因此，有必要对这些多样的人体图像生成方法进行系统综述。本文将人体图像生成技术划分为三大范式，即数据驱动方法、知识引导方法和混合方法。针对每种范式，介绍了最具代表性的模型及其变体，并从模型架构角度总结了不同方法的优势与特点。此外，归纳了文献中主要公开的人体图像数据集与评价指标。同时，鉴于合成人体图像广泛的应用潜力，本文涵盖了其典型下游应用。最后，讨论了人体图像生成面临的挑战与潜在机遇，为未来研究提供启示。