Efficient content-based retrieval of motion capture data
ACM SIGGRAPH 2005 Papers
Perceptually consistent example-based human motion retrieval
Proceedings of the 2009 symposium on Interactive 3D graphics and games
Efficient motion data indexing and retrieval with local similarity measure of motion strings
The Visual Computer: International Journal of Computer Graphics
Indexing 3-D human motion repositories for content-based retrieval
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
A Quick Filtering for Similarity Queries in Motion Capture Databases
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Hierarchical indexing structure for 3d human motions
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Content-Based human motion retrieval with automatic transition
CGI'06 Proceedings of the 24th international conference on Advances in Computer Graphics
Efficient motion search in large motion capture databases
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
| Hi-index | 0.00 |
In recent years, with the development and increasingly mature of motion capture technology, it has become one of the most widely used technologies to obtain realistic human motion in computer animation. With the increasing demands, motion dataset is becoming larger and larger. Due to motion feature data have the high-dimensional complexity, we first adopt nonlinear ISOMAP manifold learning algorithm to resolve the "curse of dimensionality" problem for motion feature data. In order to save the time of reducing dimension, we adopt the scarcity of neighboring-graph to improve ISOMAP algorithm for making it apply the massive human motion database. Then we build a motion string index for database, deploy Smith-Waterman algorithm to compare the retrieval samples' motion string with motion strings of candidate datasets, finally, we obtain the similar motion sequence. Experiment results show that the approach proposed in this paper is effective and efficient.