| dc.description.abstract | This dissertation takes performance-driven character animation as a representative application and advances motion capture algorithms and animation methods to meet its high demands. Existing approaches have either coarse resolution and restricted capture volume, require expensive and complex multi-camera systems, or use intrusive suits and controllers.
For motion capture, set-up time is reduced using fewer cameras, accuracy is increased despite occlusions and general environments, initialization is automated, and free roaming is enabled by egocentric cameras. For animation, increased robustness enables the use of low-cost sensors input, custom control gesture definition is guided to support novice users, and animation expressiveness is increased. The important contributions are:
1) an analytic and differentiable visibility model for pose optimization under strong occlusions,
2) a volumetric contour model for automatic actor initialization in general scenes,
3) a method to annotate and augment image-pose databases automatically,
4) the utilization of unlabeled examples for character control, and
5) the generalization and disambiguation of cyclical gestures for faithful character animation.
In summary, the whole process of human motion capture, processing, and application to animation is advanced. These advances on the state of the art have the potential to improve many interactive applications, within and outside virtual reality. | en_US |
