  We present an implicit neural representation to learn the spatio-temporal
space of kinematic motions. Unlike previous work that represents motion as
discrete sequential samples, we propose to express the vast motion space as a
continuous function over time, hence the name Neural Motion Fields (NeMF).
Specifically, we use a neural network to learn this function for miscellaneous
sets of motions, which is designed to be a generative model conditioned on a
temporal coordinate $t$ and a random vector $z$ for controlling the style. The
model is then trained as a Variational Autoencoder (VAE) with motion encoders
to sample the latent space. We train our model with a diverse human motion
dataset and quadruped dataset to prove its versatility, and finally deploy it
as a generic motion prior to solve task-agnostic problems and show its
superiority in different motion generation and editing applications, such as
motion interpolation, in-betweening, and re-navigating. More details can be
found on our project page: https://cs.yale.edu/homes/che/projects/nemf/.
