  We propose real-time, six degrees of freedom (6DoF), 3D face pose estimation
without face detection or landmark localization. We observe that estimating the
6DoF rigid transformation of a face is a simpler problem than facial landmark
detection, often used for 3D face alignment. In addition, 6DoF offers more
information than face bounding box labels. We leverage these observations to
make multiple contributions: (a) We describe an easily trained, efficient,
Faster R-CNN--based model which regresses 6DoF pose for all faces in the photo,
without preliminary face detection. (b) We explain how pose is converted and
kept consistent between the input photo and arbitrary crops created while
training and evaluating our model. (c) Finally, we show how face poses can
replace detection bounding box training labels. Tests on AFLW2000-3D and BIWI
show that our method runs at real-time and outperforms state of the art (SotA)
face pose estimators. Remarkably, our method also surpasses SotA models of
comparable complexity on the WIDER FACE detection benchmark, despite not been
optimized on bounding box labels.
