Real-time animation of human characters with fuzzy controllers

TL;DR

This paper presents a real-time procedural animation system for human characters using a fuzzy neural network model, allowing both machine learning and artist tuning, demonstrated on gait animations.

Contribution

It introduces a fuzzy neural network model for real-time human animation synthesis that can be tuned by machine learning or artists, enhancing adaptability.

Findings

Capable of synthesizing gait animations for various limb and step sizes

Uses motion capture data for training and tuning

Demonstrates real-time animation on flat and inclined terrains

Abstract

The production of animation is a resource intensive process in game companies. Therefore, techniques to synthesize animations have been developed. However, these procedural techniques offer limited adaptability by animation artists. In order to solve this, a fuzzy neural network model of the animation is proposed, where the parameters can be tuned either by machine learning techniques that use motion capture data as training data or by the animation artist himself. This paper illustrates how this real time procedural animation system can be developed, taking the human gait on flat terrain and inclined surfaces as example. Currently, the parametric model is capable of synthesizing animations for various limb sizes and step sizes.