NRDF: Neural Riemannian Distance Fields for Learning Articulated Pose Priors
CVPR 2024(2024)
摘要
Faithfully modeling the space of articulations is a crucial task that allows
recovery and generation of realistic poses, and remains a notorious challenge.
To this end, we introduce Neural Riemannian Distance Fields (NRDFs),
data-driven priors modeling the space of plausible articulations, represented
as the zero-level-set of a neural field in a high-dimensional
product-quaternion space. To train NRDFs only on positive examples, we
introduce a new sampling algorithm, ensuring that the geodesic distances follow
a desired distribution, yielding a principled distance field learning paradigm.
We then devise a projection algorithm to map any random pose onto the level-set
by an adaptive-step Riemannian optimizer, adhering to the product manifold of
joint rotations at all times. NRDFs can compute the Riemannian gradient via
backpropagation and by mathematical analogy, are related to Riemannian flow
matching, a recent generative model. We conduct a comprehensive evaluation of
NRDF against other pose priors in various downstream tasks, i.e., pose
generation, image-based pose estimation, and solving inverse kinematics,
highlighting NRDF's superior performance. Besides humans, NRDF's versatility
extends to hand and animal poses, as it can effectively represent any
articulation.
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