Unsupervised Cross-Dataset Adaptation via Probabilistic Amodal 3D Human Pose Completion

Despite remarkable success of supervised deep learning models for 3D human pose estimation, performance of such models is mostly limited to constrained laboratory settings. Such models not only exhibit an alarming level of dataset bias, but also fail to operate on unconstrained videos in the presence of external variations such as camera motion, partial body visibility, occlusion, etc. Acknowledging these shortcomings, firstly, we aim to formalize a motion representation learning framework by effectively utilizing both constrained and artificially generated unconstrained video samples for datasets with 3D pose annotation. Without ignoring the inherent uncertainty in pose estimation for the truncated video frames, we devise a novel probabilistic amodal pose completion framework to enable generation of multiple plausible pose-filling outcomes. Secondly, to address dataset bias, the probabilistic amodal framework is reutilized to design novel self-supervised objectives. This not only enables adaptation of the model to target unannotated datasets (wild YouTube videos) but also encourages learning of generic motion representations beyond the available supervised data even in unconstrained scenarios. Such a training regime helps us achieve state-of-the art performance on unsupervised cross-dataset pose estimation, with a significant improvement in partially-visible unconstrained scenarios.