Recent advances in deep learning approaches to computer vision problems have led to renewed interest in the task of predicting 3D human joint locations from raw image data, with application areas including sports analysis, human-computer interaction, and physical rehabilitation. Although supervised learning of deep neural networks has proven to be effective for pose estimation, it requires a wealth of varied data to generalise well to previously unseen examples. Consequently, progress in 3D human pose estimation has been slowed by the fact that 3D keypoint annotations are notoriously difficult to obtain, traditionally requiring a large array of cameras and/or the use of wearable markers/sensors. In this paper we describe a methodology for obtaining 3D human pose annotations using only three video cameras and without any wearables. We apply this methodology to construct ASPset-510 (Australian Sports Pose Dataset), a large collection of natural sports-related video with 3D pose annotations. Using ASPset-510 as an additional source of training examples, we found that we could improve pose model generalisation on the established MPI-INF-3DHP benchmark. We make ASPset-510 publicly available, and provide strong baseline results for future work to compare against.

深度学习方法在解决计算机视觉问题方面的最新进展引起了人们对从原始图像数据预测3D人体关节位置的兴趣重新产生兴趣，其应用领域包括运动分析，人机交互和身体康复。尽管深度神经网络的监督学习已被证明对于姿势估计是有效的，但它需要大量多样的数据才能很好地概括到以前看不见的例子。因此，众所周知，很难获得3D关键点注释，这在传统上需要大量的摄像头和/或使用可穿戴的标记/传感器，这一事实已经减慢了3D人体姿势估计的进度。在本文中，我们描述了仅使用三个摄像机且没有任何可穿戴设备来获得3D人体姿势注解的方法。我们采用这种方法来构建ASPset-510（澳大利亚体育姿势数据集），这是一个包含3D姿势注释的自然体育相关视频的大量集合。使用ASPset-510作为培训示例的其他来源，我们发现可以在已建立的MPI-INF-3DHP基准测试中改善姿态模型的通用性。我们使ASPset-510公开可用，并提供强大的基线结果供以后的工作进行比较。