Generalizing motion edits with Gaussian processes
Abstract
One way that artists create compelling character animations is by manipulating details of a character's motion. This process is expensive and repetitive. We show that we can make such motion editing more efficient by generalizing the edits an animator makes on short sequences of motion to other sequences. Our method predicts frames for the motion using Gaussian process models of kinematics and dynamics. These estimates are combined with probabilistic inference. Our method can be used to propagate edits from examples to an entire sequence for an existing character, and it can also be used to map a motion from a control character to a very different target character. The technique shows good generalization. For example, we show that an estimator, learned from a few seconds of edited example animation using our methods, generalizes well enough to edit minutes of character animation in a high-quality fashion. Learning is interactive: An animator who wants to improve the output can provide small, correcting examples and the system will produce improved estimates of motion. We make this interactive learning process efficient and natural with a fast, full-body IK system with novel features. Finally, we present data from interviews with professional character animators that indicate that generalizing and propagating animator edits can save artists significant time and work.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2009
- Source ID
- 10.1145/1477926.1477927
Entities
People
- David Forsyth
- Leslie Ikemoto
- Okan Arikan
Organizations
- Office of Naval Research
- University of California, Berkeley
- University of Illinois Urbana–Champaign
- University of Texas at Austin