Augmenting Gesture Animation with Motion Capture Data to Provide Full-Body Engagement

摘要

Effective speakers engage their whole body when they gesture. It is difficult, however, to create such full body motion in animated agents while still supporting a large and flexible gesture set. This paper presents a hybrid system that combines motion capture data with a procedural animation system for arm gestures. Procedural approaches are well suited to supporting a large and easily modified set of gestures, but are less adept at producing subtle, full body movement. Our system aligns small motion capture samples of lower body movement, and procedurally generated spine rotation, with gesture strokes to create convincing full-body movement. A combined prediction model based on a Markov model and association rules is used to select these clips. Given basic information on the stroke, the system is fully automatic. A user study compares three cases: the model turned off, and two variants of our algorithm. Both versions of the model were shown to be preferable to no model and guidance is given on which variant is preferable.