It is necessary to analyze the whole-body kinematics (including joint locations and joint angles) to assess risks of fatal and musculoskeletal injuries in occupational tasks. Human pose estimation has gotten more attention in recent years as a method to minimize the errors in determining joint locations. However, the joint angles are not often estimated, nor is the quality of joint angle estimation assessed. In this paper, we presented an end-to-end approach on direct joint angle estimation from multi-view images. Our method leveraged the volumetric pose representation and mapped the rotation representation to a continuous space where each rotation was uniquely represented. We also presented a new kinematic dataset in the domain of residential roofing with a data processing pipeline to generate necessary annotations for the supervised training procedure on direct joint angle estimation. We achieved a mean angle error of $7.19^\circ$ on the new Roofing dataset and $8.41^\circ$ on the Human3.6M dataset, paving the way for employment of on-site kinematic analysis using multi-view images.

翻译：分析全身运动学（包括关节位置和关节角度）对于评估职业性任务中致命及肌肉骨骼损伤风险至关重要。近年来，人体姿态估计作为一种减少关节定位误差的方法受到广泛关注。然而，关节角度通常未被估计，其估计质量也缺乏评估。本文提出了一种从多视角图像直接估计关节角度的端到端方法。该方法利用体积姿态表示，并将旋转表示映射到连续空间中，使每个旋转角度具有唯一表示。我们还提出了一个面向住宅屋顶作业场景的运动学数据集，并配套设计了数据预处理流程，为基于直接关节角度估计的监督训练生成所需标注。我们在新构建的Roofing数据集上实现了$7.19^\circ$的平均角度误差，在Human3.6M数据集上实现了$8.41^\circ$的平均角度误差，为利用多视角图像进行现场运动学分析铺平了道路。