End-to-end deep representation learning has achieved remarkable accuracy for
monocular 3D human pose estimation, yet these models may fail for unseen poses
with limited and fixed training data. This paper proposes a novel data
augmentation method that: (1) is scalable for synthesizing massive amount of
training data (over 8 million valid 3D human poses with corresponding 2D
projections) for training 2D-to-3D networks, (2) can effectively reduce dataset
bias. Our method evolves a limited dataset to synthesize unseen 3D human
skeletons based on a hierarchical human representation and heuristics inspired
by prior knowledge. Extensive experiments show that our approach not only
achieves state-of-the-art accuracy on the largest public benchmark, but also
generalizes significantly better to unseen and rare poses. Code, pre-trained
models and tools are available at this HTTPS URL.

本研究提出了一种新的数据增强方法，该方法基于分层的人类表示和启发式先验知识，并且可以有效地合成未看见的三维人类骨骼，用于训练二维到三维网络，实验结果表明，我们的方法不仅在最大的公共基准测试中取得了最先进的准确性，而且对未见过的和罕见的姿势的泛化能力也得到了显著提高。