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.

We describe a discriminative method for distinguishing natural-looking from unnatural-looking motion. Our method is based on physical and data-driven features of motion to which humans seem sensitive. We demonstrate that our technique is significantly more accurate than current alternatives.

Footskate, where a character's foot slides on the ground when it should be planted firmly, is a common artifact resulting from almost any attempt to modify motion capture data. We describe an online method for fixing footskate that requires no manual clean-up. An important part of fixing footskate is determining when the feet should be planted. We introduce an oracle that can automatically detect when foot plants should occur. Our method is more accurate than baseline methods that check the height or speed of the feet. These baseline methods perform especially poorly on noisy or imperfect data, requiring manual fixing. Once trained, our oracle is robust and can be used without manual clean-up, making it suitable for large databases of motion. After the foot plants are detected we use an off-the-shelf inverse kinematics based method to maintain ground contact during each foot plant. Our foot plant detection mechanism coupled with an IK based fixer can be treated as a black box that produces natural-looking motion of the feet, making it suitable for interactive systems. We demonstrate several applications which would produce unrealistic motion without our method.