
Faithfully modeling the space of articulations is crucial for recovery and generation of realistic poses, introducing Neural Riemannian Distance Fields (NRDFs) as data-driven priors modeling the space of plausible articulations.


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nrdf-neural-riemannian-distance-fields-for-learning-articulated-pose-priors


NRDF: Neural Riemannian Distance Fields for Learning Articulated Pose Priors


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データ駆動の事前モデリングにより、可能な関節姿勢の空間を表現するための原理的な手法であるNeural Riemannian Distance Fields（NRDF）が導入されました。



NRDF introduces a principled method to model data-driven priors on high-dimensional Riemannian manifolds, offering superior performance in various pose-related tasks. The approach involves training NRDFs on positive examples only, ensuring geodesic distances follow desired distributions.


Neural-Riemannian-Distance-Fields-for-Learning-Articulated-Pose-Priors


Neural Riemannian Distance Fields for Learning Articulated Pose Priors
