Landmark-Guided Elastic Shape Analysis of Human Character Motions
This work addresses the need for better postprocessing, interpolation, and classification of motion-captured animations in movies and video games, representing an incremental improvement in domain-specific methods.
The paper tackles the problem of processing and aligning human character animations by extending the elastic metric model from shape analysis with inexact feature point information, resulting in improved temporal alignment of different animations.
Motions of virtual characters in movies or video games are typically generated by recording actors using motion capturing methods. Animations generated this way often need postprocessing, such as improving the periodicity of cyclic animations or generating entirely new motions by interpolation of existing ones. Furthermore, search and classification of recorded motions becomes more and more important as the amount of recorded motion data grows. In this paper, we will apply methods from shape analysis to the processing of animations. More precisely, we will use the by now classical elastic metric model used in shape matching, and extend it by incorporating additional inexact feature point information, which leads to an improved temporal alignment of different animations.