Believable Automatically Synthesized Motion by Knowledge-Enhanced Motion Transformation
Abstract
In this thesis we develop a new automatic synthesis algorithm based on motion transformation, which produces new motions by combining and/or deforming existing motions. Current motion transformation techniques are low to mid level tools that are limited in the range and/or precision of deformations they can make to a motion or groups of motions. We believe these limitations follow from treating the motions as largely unstructured collections of signals. Consequently, the first contribution of our work is to create a powerful, general motion transformation algorithm that combines the strengths of previous techniques by structuring input motions in a way that allows us to combine the effects of several transformation techniques. To utilize this algorithm in an automatic setting, we must be able to encapsulate control rules in primitive motion generators. We accomplish this by developing the "motion model," which contains rules for transforming sets of example motions for a specific class of action. We show that because the example motions already contain detailed information about the action, the rules can be formulated on general properties of the action, such as targets/goals, rather than low-level properties such as muscle activations. This not only makes the rules relatively easy to devise, but allows a single motion model to generate motion in any style for which we can provide a few example motions. In the course of our experimentation we developed fifteen different motion models for humanoid character animation, several of which possess multiple styles (mainly derived from motion captured data).
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 21, 2000
- Accession Number
- ADA387173
Entities
People
- F. S. Grassia
Organizations
- Carnegie Mellon University