The best performing methods for 3D human pose estimation from monocular
images require large amounts of in-the-wild 2D and controlled 3D pose annotated
datasets which are costly and require sophisticated systems to acquire. To
reduce this annotation dependency, we propose Multiview-Consistent Semi
Supervised Learning (MCSS) framework that utilizes similarity in pose
information from unannotated, uncalibrated but synchronized multi-view videos
of human motions as additional weak supervision signal to guide 3D human pose
regression. Our framework applies hard-negative mining based on temporal
relations in multi-view videos to arrive at a multi-view consistent pose
embedding. When jointly trained with limited 3D pose annotations, our approach
improves the baseline by 25% and state-of-the-art by 8.7%, whilst using
substantially smaller networks. Lastly, but importantly, we demonstrate the
advantages of the learned embedding and establish view-invariant pose retrieval
benchmarks on two popular, publicly available multi-view human pose datasets,
Human 3.6M and MPI-INF-3DHP, to facilitate future research.

该论文提出了一种基于多视角视频的多视角一致半监督学习框架，利用未经注释、未校准但同步的多视角视频中的姿态信息相似性作为额外的弱监督信号来引导 3D 人体姿势回归，该方法通过硬负采样来建立多视角一致的姿态嵌入，并结合有限的 3D 姿态注释来完善该模型，从而在两种公开数据集上实现了视角不变的姿态检索。