Open Collections

Style exploration and generalization for character animation

University of British Columbia

Believable character animation arises from a well orchestrated performance by a digital character. Various techniques have been developed to help drive this performance in an effort to create believable character animations. However, automatic style exploration and generalization from motion data are still open problems. We tackle several different aspects of the motion generation problem which aim to advance the state of the art in the areas of style exploration and generalization. First, we describe a novel optimization framework that produces a diverse range of motions for physics-based characters for tasks such as jumps, flips, and walks. This stands in contrast to the more common use of optimization to produce a single optimal motion. The solutions can be optimized to achieve motion diversity or diversity in the proportions of the simulated characters. Exploration of style of task achievement for physics-based character animation can be performed automatically by exploiting ``null spaces'' defined by the task. Second, we perform automatic style generalization by generalizing a controller for varying degree of task achievement for a specified task. We describe an exploratory approach which explores trade-offs between competing objectives for a specified task. Pareto-optimality can be used to explore various degrees of task achievement for a given style of physics-based character animation. We describe our algorithms for computing a set of controllers that span the pareto-optimal front for jumping motions which explore the trade-off between effort and jump height. We also develop supernatural jump controllers through the optimized introduction of external forces. Third, we develop a data-driven approach to model sub-steps, such as, sliding foot pivots and foot shuffling. These sub-steps are often an integral component of the style observed in task-specific locomotion. We present a model for generating these sub-steps via a foot step planning algorithm which is then used to generate full body motion. The system is able to generalize the style observed in task-specific locomotion to novel scenarios.

Text

2015-12-17

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/55853

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/