2022/06/04 by Chengan He, Jun Saito, He, Chengan +8 · 1 voice · 12 citations
Computer Science · Engineering · #Human Motion and Animation #Human Pose and Action Recognition #Video Analysis and Summarization #cs.CV #cs.GR
paper · pdf · doi:10.48550/arxiv.2206.03287
openalex publication_date 2022/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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/.