  Existing head pose estimation datasets are either composed of numerous
samples by non-realistic synthesis or lab collection, or limited images by
labor-intensive annotating. This makes deep supervised learning based solutions
compromised due to the reliance on generous labeled data. To alleviate it, we
propose the first semi-supervised unconstrained head pose estimation (SemiUHPE)
method, which can leverage a large amount of unlabeled wild head images.
Specifically, we follow the recent semi-supervised rotation regression, and
focus on the diverse and complex head pose domain. Firstly, we claim that the
aspect-ratio invariant cropping of heads is superior to the previous
landmark-based affine alignment, which does not fit unlabeled natural heads or
practical applications where landmarks are often unavailable. Then, instead of
using an empirically fixed threshold to filter out pseudo labels, we propose
the dynamic entropy-based filtering by updating thresholds for adaptively
removing unlabeled outliers. Moreover, we revisit the design of weak-strong
augmentations, and further exploit its superiority by devising two novel
head-oriented strong augmentations named pose-irrelevant cut-occlusion and
pose-altering rotation consistency. Extensive experiments show that SemiUHPE
can surpass SOTAs with remarkable improvements on public benchmarks under both
front-range and full-range. Our code is released in
\url{https://github.com/hnuzhy/SemiUHPE}.
