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.

随着深度生成模型的发展，图像和视频合成因其巨大的学术和应用价值而成为计算机视觉和机器学习领域的热门话题。许多研究人员一直致力于合成高保真人体图像作为日常生活中最常见的物体类别之一，基于各种模型、任务设置和应用程序进行了大量研究。因此，有必要对这些不同的人类图像生成方法进行全面的概述。在本文中，我们将人类图像生成技术分为三个范式，即数据驱动方法、知识引导方法和混合方法。对于每个范式，都给出了最具代表性的模型和相应的变体，其中根据模型架构总结了不同方法的优点和特征。此外，还总结了文献中主要的公共人体图像数据集和评估指标。此外，由于广泛的应用潜力，合成人类图像的典型下游用途也被涵盖。最后，讨论了人类图像生成的挑战和潜在机遇，以阐明未来的研究。