NeMF: Neural Motion Fields for Kinematic Animation

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 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/Comment: Our project page is available at: https://cs.yale.edu/homes/che/projects/nemf

Clustering and Volume Scattering for Hierarchical Radiosity Calculations

This paper introduces a new approach to hierarchical radiosity computation, making it practical for the simulation of energy exchanges in very complex environments. Results indicate that the new formulation allows the effective simulation of environments of significant complexity, containing several thousands of surfaces or volumes. In this new technique a hierarchy is constructed in a bottom-up fashion, in effect grouping together nearby surfaces for the purpose of evaluating their energy exchanges with distant objects. This clustering approach eliminates the need for an O(