Parametric motion graphs

Rachel Heck, Michael Gleicher

2007 · 208 citations · 34 references

Concepts

TL;DR

The paper introduces an example-based motion synthesis technique that produces continuous, high-fidelity, controllable motion streams for interactive applications like video games. The method employs a parametric motion graph data structure that blends motion clips via sampling-based transition identification, enabling real-time generation of any motion from a continuous parameter space with minimal user effort. Parametric motion graphs, by organizing motions into a single parameterized node, enable fast decision-making and allow interactive characters to perform complex action sequences such as cartwheeling or punching.

Abstract

In this paper, we present an example-based motion synthesis technique that generates continuous streams of high-fidelity, controllable motion for interactive applications, such as video games. Our method uses a new data structure called a parametric motion graph to describe valid ways of generating linear blend transitions between motion clips dynamically generated through parametric synthesis in realtime. Our system specifically uses blending-based parametric synthesis to accurately generate any motion clip from an entire space of motions by blending together examples from that space. The key to our technique is using sampling methods to identify and represent good transitions between these spaces of motion parameterized by a continuously valued parameter. This approach allows parametric motion graphs to be constructed with little user effort. Because parametric motion graphs organize all motions of a particular type, such as reaching to different locations on a shelf, using a single, parameterized graph node, they are highly structured, facilitating fast decision-making for interactive character control. We have successfully created interactive characters that perform sequences of requested actions, such as cartwheeling or punching.

References

34