Human pose estimation aims to locate the human body parts and build human body representation (e.g., body skeleton) from input data such as images and videos. It has drawn increasing attention during the past decade and has been utilized in a wide range of applications including human-computer interaction, motion analysis, augmented reality, and virtual reality. Although the recently developed deep learning-based solutions have achieved high performance in human pose estimation, there still remain challenges due to insufficient training data, depth ambiguities, and occlusions. The goal of this survey paper is to provide a comprehensive review of recent deep learning-based solutions for both 2D and 3D pose estimation via a systematic analysis and comparison of these solutions based on their input data and inference procedures. More than 240 research papers since 2014 are covered in this survey. Furthermore, 2D and 3D human pose estimation datasets and evaluation metrics are included. Quantitative performance comparisons of the reviewed methods on popular datasets are summarized and discussed. Finally, the challenges involved, applications, and future research directions are concluded. We also provide a regularly updated project page on: \url{https://github.com/zczcwh/DL-HPE}

中文翻译：

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基于深度学习的人体姿势估计：一项调查

人体姿势估计的目的是根据诸如图像和视频之类的输入数据来定位人体部位并建立人体表征（例如，人体骨骼）。在过去的十年中，它引起了越来越多的关注，并已被广泛用于包括人机交互，运动分析，增强现实和虚拟现实的应用中。尽管最近开发的基于深度学习的解决方案在人体姿势估计方面取得了高性能，但是由于训练数据不足，深度模糊和遮挡，仍然存在挑战。本调查论文的目的是通过基于输入和推理程序的系统分析和比较，对基于深度学习的2D和3D姿态估计解决方案进行全面回顾。自2014年以来，该调查涵盖了240余篇研究论文。此外，还包括2D和3D人体姿势估计数据集和评估指标。总结并讨论了所审查方法在流行数据集上的定量性能比较。最后，总结了所涉及的挑战，应用和未来的研究方向。我们还在以下位置提供了定期更新的项目页面：\ url {https://github.com/zczcwh/DL-HPE}