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.
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."