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Neural Riemannian Distance Fields for Learning Articulated Pose Priors
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.
NRDF: Neural Riemannian Distance Fields for Learning Articulated Pose Priors
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: Neural Riemannian Distance Fields for Learning Articulated Pose Priors
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.
