  We present Pose-NDF, a continuous model for plausible human poses based on
neural distance fields (NDFs). Pose or motion priors are important for
generating realistic new poses and for reconstructing accurate poses from noisy
or partial observations. Pose-NDF learns a manifold of plausible poses as the
zero level set of a neural implicit function, extending the idea of modeling
implicit surfaces in 3D to the high-dimensional domain SO(3)^K, where a human
pose is defined by a single data point, represented by K quaternions. The
resulting high-dimensional implicit function can be differentiated with respect
to the input poses and thus can be used to project arbitrary poses onto the
manifold by using gradient descent on the set of 3-dimensional hyperspheres. In
contrast to previous VAE-based human pose priors, which transform the pose
space into a Gaussian distribution, we model the actual pose manifold,
preserving the distances between poses. We demonstrate that PoseNDF outperforms
existing state-of-the-art methods as a prior in various downstream tasks,
ranging from denoising real-world human mocap data, pose recovery from occluded
data to 3D pose reconstruction from images. Furthermore, we show that it can be
used to generate more diverse poses by random sampling and projection than
VAE-based methods.
