The standard approach to tackling computer vision problems is to train deep convolutional neural network (CNN) models using large-scale image datasets which are representative of the target task. However, in many scenarios, it is often challenging to obtain sufficient image data for the target task. Data augmentation is a way to mitigate this challenge. A common practice is to explicitly transform existing images in desired ways so as to create the required volume and variability of training data necessary to achieve good generalization performance. In situations where data for the target domain is not accessible, a viable workaround is to synthesize training data from scratch--i.e., synthetic data augmentation. This paper presents an extensive review of synthetic data augmentation techniques. It covers data synthesis approaches based on realistic 3D graphics modeling, neural style transfer (NST), differential neural rendering, and generative artificial intelligence (AI) techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs). For each of these classes of methods, we focus on the important data generation and augmentation techniques, general scope of application and specific use-cases, as well as existing limitations and possible workarounds. Additionally, we provide a summary of common synthetic datasets for training computer vision models, highlighting the main features, application domains and supported tasks. Finally, we discuss the effectiveness of synthetic data augmentation methods. Since this is the first paper to explore synthetic data augmentation methods in great detail, we are hoping to equip readers with the necessary background information and in-depth knowledge of existing methods and their attendant issues.

翻译：解决计算机视觉问题的标准方法是使用代表目标任务的大规模图像数据集训练深度卷积神经网络（CNN）模型。然而，在许多场景中，获取足够的目标任务图像数据往往具有挑战性。数据增强是缓解这一挑战的一种方式。常见做法是显式地以期望方式变换现有图像，以产生训练所需的数据量和多样性，从而获得良好的泛化性能。在目标域数据不可获取的情况下，一种可行的替代方案是从零开始合成训练数据，即合成数据增强。本文对合成数据增强技术进行了全面综述，涵盖基于逼真3D图形建模、神经风格迁移（NST）、差分神经渲染以及生成式人工智能（AI）技术（如生成对抗网络（GAN）和变分自编码器（VAE））的数据合成方法。针对每类方法，我们重点关注重要的数据生成与增强技术、通用应用范围及具体用例，以及现有局限性和可能的解决方案。此外，我们总结了用于训练计算机视觉模型的常见合成数据集，突出其主要特征、应用领域和支持的任务。最后，讨论了合成数据增强方法的有效性。由于这是首篇深入探讨合成数据增强方法的论文，我们希望为读者提供必要的背景知识，并对现有方法及其相关问题形成深入理解。