No abstract

Dynamic response is a technique for employing a physical reaction to an animated character. The technique utilizes a database of reactions as example motions to transition to following a dynamic simulation of an interaction. The search for the example to follow has been the stumbling block for bringing such a system into realtime applications and in this paper, we address that issue by proposing a number of speed-ups which make the approach faster and more appropriate for an electronic game implementation. We accomplish our speed-up by using a supervised learning routine which trains offline on a large set of dynamic response examples and predicts online among the choices found in the database. Also, we propose a near-optimal routine which finds the alignment of the selected motion for the given scenario based on a sparse sampling with an additional speed-up over the original algorthim. With both of these changes in place, we enjoy a tremendous speed-up with inperceptable difference in the final motion compared to previous published results. Finally we offer a few additional alternatives that allow the user to choose between quality and speed based on their individual needs.

Abstract. We introduce a general method for animating controlled stepping motion for use in combining motion capture sequences. Our stepping algorithm is characterized by two simple models which idealize the movement of the stepping foot and the projected center of mass based on observations from a database of step motions. We draw a parallel between stepping and point-to-point reaching to motivate our foot model and employ an inverted pendulum model common in robotics for the center of mass. Our system computes path and speed profiles from each model and then adapts an interpolation to follow the synthesized trajectories in the final motion. We show that our animations can be enriched through the use of step examples, but also that we can synthesize stepping to create transitions between existing segments without the need for a motion example. We demonstrate that our system can generate precise, realistic stepping for a number of scenarios.

No abstract

Automatically generated anticipation is a largely overlooked component of response in character motion for computer animation. We present an approach for generating anticipation to unexpected interactions with examples taken from human motion capture data. Our system generates animation by quickly selecting an anticipatory action using a Support Vector Machine (SVM) which is trained offline to distinguish the characteristics of a given scenario according to a metric that assesses predicted damage and energy expenditure for the character. We show our results for a character that can anticipate by blocking or dodging a threat coming from a variety of locations and targeting any part of the body, from head to toe.