We present a new approach for 3D human pose estimation from a single image. State-of-the-art methods for 3D pose estimation have focused on predicting a full-body pose of a single person and have not given enough attention to the challenges in application: incompleteness of body pose and existence of multiple persons in image. In this paper, we introduce depth maps to solve these problems. Our approach predicts the depths of human pose over all spatial grids, which supports 3D poses estimation for incomplete or full bodies of multiple persons. The proposed depth maps encode depths of limbs rather than joints. They are more informative and reversibly convertible to depths of joints. The unified network is trained end to end using mixed 2D and 3D annotated samples. The experiments reveal that our algorithm achieves the state of the art on Human3.6M, the largest publicly available 3D pose estimation benchmark. Moreover, qualitative results have been reported to demonstrate the effectiveness of our approach for 3D pose estimation for incomplete human bodies and multiple persons.

中文翻译：

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基于深度图的 3D 人体姿态估计

我们提出了一种从单个图像估计 3D 人体姿势的新方法。最先进的 3D 姿态估计方法主要集中在预测单个人的全身姿态，而没有对应用中的挑战给予足够的重视：身体姿态的不完整和图像中存在多人。在本文中，我们引入深度图来解决这些问题。我们的方法预测了所有空间网格上人体姿势的深度，这支持对多个人的不完整或全身的 3D 姿势估计。建议的深度图编码四肢的深度而不是关节。它们提供更多信息，并且可以可逆地转换为关节深度。统一网络使用混合的 2D 和 3D 注释样本进行端到端训练。实验表明，我们的算法在 Human3.6M 上达到了最先进的水平，最大的公开可用的 3D 姿态估计基准。此外，已经报告了定性结果，以证明我们的方法对不完整人体和多人的 3D 姿态估计的有效性。