Core Concepts
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.
Abstract
NRDFs are introduced as a method to learn data-driven priors on high-dimensional Riemannian manifolds.
The article discusses the challenges in modeling articulated poses and the importance of understanding human pose.
NRDFs are trained on positive examples using a new sampling algorithm to ensure desired geodesic distances.
The article explains the projection algorithm to map random poses onto the level-set by an adaptive-step Riemannian optimizer.
NRDFs are evaluated against other pose priors in various tasks, showing superior performance.
The versatility of NRDF extends to hand and animal poses, effectively representing any articulation.
Stats
NRDFs는 고차원 리만 매니폴드에서 데이터 주도의 사전을 학습하는 방법으로 소개됩니다.
새로운 샘플링 알고리즘을 사용하여 NRDFs는 양의 예제에 대해 훈련됩니다.
Quotes
"Faithfully modeling the space of articulations is a crucial task that allows recovery and generation of realistic poses."
"NRDFs are introduced as a method to learn data-driven priors on high-dimensional Riemannian manifolds."