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 recent paper, which has been accepted as a highlight at CVPR, follows on from second author Garvita Tiwari’s awardwinning work, Pose-NDF. He will present at the first poster session, on Wednesday June 19, poster 145. Computer Vision News Computer Vision News 36 CVPR 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
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