Estimating the head pose of a person is a crucial problem for numerous
applications that is yet mainly addressed as a subtask of frontal pose
prediction. We present a novel method for unconstrained end-to-end head pose
estimation to tackle the challenging task of full range of orientation head
pose prediction. We address the issue 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 allows to efficiently learn full rotation appearance and to
overcome the limitations of the current state-of-the-art. Together with new
accumulated training data that provides full head pose rotation data and a
geodesic loss approach for stable learning, we design an advanced model that is
able to predict an extended range of head orientations. An extensive evaluation
on public datasets demonstrates that our method significantly outperforms other
state-of-the-art methods in an efficient and robust manner, while its advanced
prediction range allows the expansion of the application area. We open-source
our training and testing code along with our trained models:
this https URL

使用旋转矩阵法进行端到端的头部姿态预测，通过引入连续 6D 旋转矩阵表示法和地理损失方法来有效学习头部旋转外观并克服当前最先进方法的限制，从而大幅提高头部姿态预测的范围和稳定性。

面向稳健和无约束全旋转角度头部姿态估计

Towards Robust and Unconstrained Full Range of Rotation Head Pose  Estimation

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％。