  3D human pose estimation is a key component of clinical monitoring systems.
The clinical applicability of deep pose estimation models, however, is limited
by their poor generalization under domain shifts along with their need for
sufficient labeled training data. As a remedy, we present a novel domain
adaptation method, adapting a model from a labeled source to a shifted
unlabeled target domain. Our method comprises two complementary adaptation
strategies based on prior knowledge about human anatomy. First, we guide the
learning process in the target domain by constraining predictions to the space
of anatomically plausible poses. To this end, we embed the prior knowledge into
an anatomical loss function that penalizes asymmetric limb lengths, implausible
bone lengths, and implausible joint angles. Second, we propose to filter pseudo
labels for self-training according to their anatomical plausibility and
incorporate the concept into the Mean Teacher paradigm. We unify both
strategies in a point cloud-based framework applicable to unsupervised and
source-free domain adaptation. Evaluation is performed for in-bed pose
estimation under two adaptation scenarios, using the public SLP dataset and a
newly created dataset. Our method consistently outperforms various
state-of-the-art domain adaptation methods, surpasses the baseline model by
31%/66%, and reduces the domain gap by 65%/82%. Source code is available at
https://github.com/multimodallearning/da-3dhpe-anatomy.
