This survey presents a comprehensive analysis of data augmentation techniques
in human-centric vision tasks, a first of its kind in the field. It delves into
a wide range of research areas including person ReID, human parsing, human pose
estimation, and pedestrian detection, addressing the significant challenges
posed by overfitting and limited training data in these domains. Our work
categorizes data augmentation methods into two main types: data generation and
data perturbation. Data generation covers techniques like graphic engine-based
generation, generative model-based generation, and data recombination, while
data perturbation is divided into image-level and human-level perturbations.
Each method is tailored to the unique requirements of human-centric tasks, with
some applicable across multiple areas. Our contributions include an extensive
literature review, providing deep insights into the influence of these
augmentation techniques in human-centric vision and highlighting the nuances of
each method. We also discuss open issues and future directions, such as the
integration of advanced generative models like Latent Diffusion Models, for
creating more realistic and diverse training data. This survey not only
encapsulates the current state of data augmentation in human-centric vision but
also charts a course for future research, aiming to develop more robust,
accurate, and efficient human-centric vision systems.

本调查在人相关的视觉任务中首次提供了对数据增强技术的全面分析。它深入研究了人物 ReID、人体解析、人体姿态估计和行人检测等广泛的研究领域，解决了过拟合和有限训练数据在这些领域中带来的重要挑战。我们将数据增强方法分为两类：数据生成和数据扰动。数据生成包括基于图形引擎的生成、基于生成模型的生成和数据重组等技术，而数据扰动分为图像级和人体级扰动。每种方法都针对人相关任务的独特需求进行了定制，其中一些方法适用于多个领域。我们的贡献包括广泛的文献回顾，深入洞察这些增强技术在人相关视觉中的影响，并突出每种方法的细微差别。我们还讨论了开放问题和未来方向，例如融合像潜扩散模型之类的先进生成模型，以创造更真实多样的训练数据。这项调查不仅概括了人相关视觉数据增强的当前状态，还为未来研究制定了计划，旨在开发更强大、准确和高效的人相关视觉系统。