  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.
