- Keywords: Domain adaptation, 3D pose estimation, anatomical constraints, point clouds
- Abstract: Domain adaptation has the potential to overcome the expensive or even infeasible labeling of target data by transferring knowledge from a labeled source domain. In this work, we address domain adaptation in the context of point cloud-based 3D human pose estimation, whose clinical applicability is severely limited by a lack of labeled training data. Unlike the mainstream approach of domain-invariant feature learning, we propose to guide the learning process in the target domain through weak supervision, based on prior knowledge about human anatomy. We embed this prior knowledge into a novel loss function that encourages network predictions to match the statistics of an anatomically plausible skeleton. Specifically, we formulate three loss functions that penalize asymmetric limb lengths, implausible joint angles, and implausible bone lengths. We evaluate the method on a public lying pose dataset (SLP), adapting from uncovered patients in the source to covered patients in the target domain. Our method outperforms diverse state-of-the-art domain adaptation techniques and improves the baseline model by 26% while reducing the gap to a fully supervised model by 54%. Source code is available at https://github.com/multimodallearning/da-3dhpe-anatomy.
- Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
- Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
- Paper Type: methodological development
- Primary Subject Area: Transfer Learning and Domain Adaptation
- Secondary Subject Area: Application: Other
- Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
- Code And Data: Data: https://web.northeastern.edu/ostadabbas/2019/06/27/multimodal-in-bed-pose-estimation/ Code: https://github.com/multimodallearning/da-3dhpe-anatomy