May 14, 2017 02:21


Speaker: Prof. Taku Komura (University of Edinburgh, UK)

Title: Phase-Functioned Neural Networks for Character Control

We present a real-time character control mechanism using a novel neural
network architecture called a Phase-Functioned Neural Network. In this
network structure, the weights are computed via a cyclic function which
uses the phase as an input. Along with the phase, our system takes as input
user controls, the previous state of the character, the geometry of the scene,
and automatically produces high quality motions that achieve the desired
user control. The entire network is trained in an end-to-end fashion on a
large dataset composed of locomotion such as walking, running, jumping,
and climbing movements fitted into virtual environments. Our system can
therefore automatically produce motions where the character adapts to
different geometric environments such as walking and running over rough
terrain, climbing over large rocks, jumping over obstacles, and crouching
under low ceilings. Our network architecture produces higher quality results
than time-series autoregressive models such as LSTMs as it deals explicitly
with the latent variable of motion relating to the phase. Once trained, our
system is also extremely fast and compact, requiring only milliseconds of
execution time and a few megabytes of memory, even when trained on
gigabytes of motion data. Our work is most appropriate for controlling
characters in interactive scenes such as computer games and virtual reality

More Information

Date June 20, 2017 (Tue) 13:30 - 15:00