In this paper, we present a method for unconstrained end-to-end head pose
estimation. We address the problem of ambiguous rotation labels by introducing
the rotation matrix formalism for our ground truth data and propose a
continuous 6D rotation matrix representation for efficient and robust direct
regression. This way, our method can learn the full rotation appearance which
is contrary to previous approaches that restrict the pose prediction to a
narrow-angle for satisfactory results. In addition, we propose a geodesic
distance-based loss to penalize our network with respect to the SO(3) manifold
geometry. Experiments on the public AFLW2000 and BIWI datasets demonstrate that
our proposed method significantly outperforms other state-of-the-art methods by
up to 20\%. We open-source our training and testing code along with our
pre-trained models: this https URL.

本文提出了一种用于无约束端到端头部姿态估计的方法，通过引入旋转矩阵形式主义来解决旋转标签的问题，并提出了连续的 6D 旋转矩阵表示来实现高效和稳健的直接回归，同时为了弥补旋转数据的几何特性，我们使用了基于测地线距离的损失函数。在公共数据集上的实验结果表明，我们每一个方面都超过了其他先进方法，提高了将近 20％。