Unsupervised Domain Adaptation for Occlusion Resilient Human Pose Estimation
Journal:
arXiv
Published Date:
Jan 6, 2025
Abstract
Occlusions are a significant challenge to human pose estimation algorithms,
often resulting in inaccurate and anatomically implausible poses. Although
current occlusion-robust human pose estimation algorithms exhibit impressive
performance on existing datasets, their success is largely attributed to
supervised training and the availability of additional information, such as
multiple views or temporal continuity. Furthermore, these algorithms typically
suffer from performance degradation under distribution shifts. While existing
domain adaptive human pose estimation algorithms address this bottleneck, they
tend to perform suboptimally when the target domain images are occluded, a
common occurrence in real-life scenarios. To address these challenges, we
propose OR-POSE: Unsupervised Domain Adaptation for Occlusion Resilient Human
POSE Estimation. OR-POSE is an innovative unsupervised domain adaptation
algorithm which effectively mitigates domain shifts and overcomes occlusion
challenges by employing the mean teacher framework for iterative pseudo-label
refinement. Additionally, OR-POSE reinforces realistic pose prediction by
leveraging a learned human pose prior which incorporates the anatomical
constraints of humans in the adaptation process. Lastly, OR-POSE avoids
overfitting to inaccurate pseudo labels generated from heavily occluded images
by employing a novel visibility-based curriculum learning approach. This
enables the model to gradually transition from training samples with relatively
less occlusion to more challenging, heavily occluded samples. Extensive
experiments show that OR-POSE outperforms existing analogous state-of-the-art
algorithms by $\sim$ 7% on challenging occluded human pose estimation datasets.
