12 DAILY CVPR Wednesday Highlight Presentation NRDF: Neural Riemannian Distance Fields for Learning Articulated Pose Priors The seeds for this project were sown when Yannan and the team behind Pose-NDF, a continuous model for plausible human poses based on neural distance fields, began to investigate the failure cases of Pose-NDF. They discovered an underlying issue in its training data distribution, which should show decreasing samples as you move away from the pose manifold. In this new paper, NRDF, Yannan aims to set that right. “We’re still modeling it as a distance field,” he explains. “During inference, it could help if you draw more samples nearby the manifold because, during the projection, you’re moving closer and closer to the manifold. If there are more negative samples near the manifold, the network prediction will be more accurate, and it’ll help you achieve more stable and accurate projection results.” The goal is to model distance fieldbased pose priors in the space of plausible articulations. The learned Yannan He is a PhD student with the Real Virtual Humans Group within the Department of Computer Science at the University of Tübingen, supervised by Gerard PonsMoll. His work, selected by area chairs to be a highlight paper, follows on from second author Garvita Tiwari’s awardwinning work, Pose-NDF. Yannan speaks to us before his poster session this morning.
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