Interactive Steering of Mesh Animations

Abstract

Creating geometrically detailed mesh animations is an involved and resource-intensive process in digital content creation. In this work we present a method to rapidly combine available sparse motion capture data with existing mesh sequences to produce a large variety of new animations. The key idea is to model shape changes correlated to the pose of the animated object via a part-based statistical shape model. We observe that compact linear models suffice for a segmentation into nearly rigid parts. The same segmentation further guides the parameterization of the pose which is learned in conjunction with the marker movement. Besides the inherent high geometric detail, further benefits of the presented method arise from its robustness against errors in segmentation and pose parameterization. Due to efficiency of both learning and synthesis phase, our model allows to interactively steer virtual avatars based on few markers extracted from video data or input devices like the Kinect sensor.