Leveraging Synthetic Adult Datasets for Unsupervised Infant Pose Estimation
Journal:
arXiv
Published Date:
Apr 8, 2025
Abstract
Human pose estimation is a critical tool across a variety of healthcare
applications. Despite significant progress in pose estimation algorithms
targeting adults, such developments for infants remain limited. Existing
algorithms for infant pose estimation, despite achieving commendable
performance, depend on fully supervised approaches that require large amounts
of labeled data. These algorithms also struggle with poor generalizability
under distribution shifts. To address these challenges, we introduce SHIFT:
Leveraging SyntHetic Adult Datasets for Unsupervised InFanT Pose Estimation,
which leverages the pseudo-labeling-based Mean-Teacher framework to compensate
for the lack of labeled data and addresses distribution shifts by enforcing
consistency between the student and the teacher pseudo-labels. Additionally, to
penalize implausible predictions obtained from the mean-teacher framework, we
incorporate an infant manifold pose prior. To enhance SHIFT's self-occlusion
perception ability, we propose a novel visibility consistency module for
improved alignment of the predicted poses with the original image. Extensive
experiments on multiple benchmarks show that SHIFT significantly outperforms
existing state-of-the-art unsupervised domain adaptation (UDA) pose estimation
methods by 5% and supervised infant pose estimation methods by a margin of 16%.
The project page is available at: https://sarosijbose.github.io/SHIFT.
