This paper addresses the challenge of 3D human pose estimation from a single
color image. Despite the general success of the end-to-end learning paradigm,
top performing approaches employ a two-step solution consisting of a
Convolutional Network (ConvNet) for 2D joint localization and a subsequent
optimization step to recover 3D pose. In this paper, we identify the
representation of 3D pose as a critical issue with current ConvNet approaches
and make two important contributions towards validating the value of end-to-end
learning for this task. First, we propose a fine discretization of the 3D space
around the subject and train a ConvNet to predict per voxel likelihoods for
each joint. This creates a natural representation for 3D pose and greatly
improves performance over the direct regression of joint coordinates. Second,
to further improve upon initial estimates, we employ a coarse-to-fine
prediction scheme. This step addresses the large dimensionality increase and
enables iterative refinement and repeated processing of the image features. The
proposed approach outperforms all state-of-the-art methods on standard
benchmarks achieving a relative error reduction greater than 30% on average.
Additionally, we investigate using our volumetric representation in a related
architecture which is suboptimal compared to our end-to-end approach, but is of
practical interest, since it enables training when no image with corresponding
3D groundtruth is available, and allows us to present compelling results for
in-the-wild images.

本研究通过对 3D 人体姿态估计表征问题的认真研究，提出了一种方法，并且对现有的最优解的表现进行了超越，在标准测试基准中相对误差降低超过 30％，该方法包括 Convolutional Network、端到端的学习模型、从粗到细的预测方案和三维体表示。