Motion normalization

Authors:
Yan Gao;Lizhuang Ma;Zhihua Chen;Xiaomao Wu
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Year:
2005

Citing 10
Cited 0

Motion signal processing

SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Motion warping

SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Adapting simulated behaviors for new characters

Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Retargetting motion to new characters

Proceedings of the 25th annual conference on Computer graphics and interactive techniques
A hierarchical approach to interactive motion editing for human-like figures

Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Computer puppetry: An importance-based approach

ACM Transactions on Graphics (TOG)
Footskate cleanup for motion capture editing

Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation
Motion graphs

Proceedings of the 29th annual conference on Computer graphics and interactive techniques
On-line Motion Retargetting

PG '99 Proceedings of the 7th Pacific Conference on Computer Graphics and Applications
A physically-based motion retargeting filter

ACM Transactions on Graphics (TOG)

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a very simple but efficient algorithm to normalize all motion data in database with same skeleton length. The input motion stream is processed sequentially while the computation for a single frame at each step requires only the results from the previous step over a neighborhood of nearby backward frames. In contrast to previous motion retargeting approaches, we simplify the constraint condition of retargeting problem, which leads to the simpler solutions. Moreover, we improve Shin et al.’s algorithm [10], which is adopted by a widely used Kovar’s footskate cleanup algorithm [6] through adding one case missed by it.