Name
Abstract | ||
|---|---|---|
In this paper, we propose an example-based approach to on-line locomotion synthesis. Our approach consists of two parts: motion analysis and motion synthesis. In the motion analysis part, an unlabeled motion sequence is first decomposed into motion segments, exploiting the behavior of the COM (center of mass) trajectory of the performer. Those motion segments are subsequently classified into groups of motion segments such that the same group of motion segments share an identical footstep pattern. Finally, we construct a hierarchical motion transition graph by representing these groups and their connectivity to other groups as nodes and edges, respectively. The coarse level of this graph models locomotive motions and their transitions, and the fine level mainly captures the cyclic nature of locomotive motions. In the motion synthesis part, given a stream of motion specifications in an on-line manner, the motion transition graph is traversed while blending the motion segments to synthesize a motion at a node, one by one, guided by the motion specifications. Our main contributions are the motion labeling scheme and a new motion model, embodied by the hierarchical motion transition graph, which together enable not only artifact-free motion blending but also seamless motion transition. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1145/1073368.1073373 | Symposium on Computer Animation 2004 |
Keywords | Field | DocType |
graph models locomotive motion,motion analysis part,motion specification,motion synthesis part,motion synthesis,on-line locomotion synthesis,hierarchical motion transition graph,artifact-free motion blending,motion modeling,motion segment,motion analysis,locomotive motion,center of mass | Structure from motion,Computer vision,Graph,Motion field,Computer science,Artificial intelligence,Motion analysis,Motion estimation,Motion modeling,Center of mass,Trajectory | Conference |
ISBN | Citations | PageRank |
1-59593-198-8 | 60 | 2.37 |
References | Authors | |
26 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taesoo Kwon | 1 | 800 | 56.37 |
| Sung Yong Shin | 2 | 1904 | 168.33 |