Existing head pose estimation (HPE) mainly focuses on single person with pre-detected frontal heads, which limits their applications in real complex scenarios with multi-persons. We argue that these single HPE methods are fragile and inefficient for Multi-Person Head Pose Estimation (MPHPE) since they rely on the separately trained face detector that cannot generalize well to full viewpoints, especially for heads with invisible face areas. In this paper, we focus on the full-range MPHPE problem, and propose a direct end-to-end simple baseline named DirectMHP. Due to the lack of datasets applicable to the full-range MPHPE, we firstly construct two benchmarks by extracting ground-truth labels for head detection and head orientation from public datasets AGORA and CMU Panoptic. They are rather challenging for having many truncated, occluded, tiny and unevenly illuminated human heads. Then, we design a novel end-to-end trainable one-stage network architecture by joint regressing locations and orientations of multi-head to address the MPHPE problem. Specifically, we regard pose as an auxiliary attribute of the head, and append it after the traditional object prediction. Arbitrary pose representation such as Euler angles is acceptable by this flexible design. Then, we jointly optimize these two tasks by sharing features and utilizing appropriate multiple losses. In this way, our method can implicitly benefit from more surroundings to improve HPE accuracy while maintaining head detection performance. We present comprehensive comparisons with state-of-the-art single HPE methods on public benchmarks, as well as superior baseline results on our constructed MPHPE datasets. Datasets and code are released in https://github.com/hnuzhy/DirectMHP.

翻译：现有头部姿态估计（HPE）主要针对单一人体且需预先检测正面头部，这限制了其在包含多人的实际复杂场景中的应用。我们认为这些单一HPE方法对于多人头部姿态估计（MPHPE）问题存在脆弱性和低效性，因为它们依赖单独训练的、难以泛化至全视角的人脸检测器，特别是针对面部区域不可见的头部。本文聚焦全范围MPHPE问题，提出名为DirectMHP的直接端到端简单基线方法。针对缺乏适用于全范围MPHPE数据集的问题，我们首先从公开数据集AGORA和CMU Panoptic中提取头部检测与头部朝向的真实标注，构建了两个基准测试集。这些数据集因包含大量存在截断、遮挡、小尺度及光照不均匀的人头而极具挑战性。随后，我们设计了一种新型端到端可训练的单阶段网络架构，通过联合回归多头的位置与朝向来解决MPHPE问题。具体而言，我们将姿态视为头部的辅助属性，附加于传统目标预测之后。该灵活设计支持任意姿态表示形式（如欧拉角）。基于共享特征和多种损失函数的联合优化，我们的方法能够隐式利用更多上下文信息提升HPE精度，同时保持头部检测性能。我们在公开基准上与最先进的单一HPE方法进行全面对比，并在构建的MPHPE数据集上获得优于基线结果。数据集与代码已开源至https://github.com/hnuzhy/DirectMHP。