Content-Based human motion retrieval with automatic transition

Authors:
Yan Gao;Lizhuang Ma;Yiqiang Chen;Junfa Liu
Affiliations:
Department of Computer Science & Engineering, Shanghai Jiao Tong University, Shanghai, P.R.C;Department of Computer Science & Engineering, Shanghai Jiao Tong University, Shanghai, P.R.C;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
CGI'06 Proceedings of the 24th international conference on Advances in Computer Graphics
Year:
2006

Citing 11
Cited 1

Motion graphs

Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive motion generation from examples

Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive control of avatars animated with human motion data

Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Rhythmic-motion synthesis based on motion-beat analysis

ACM SIGGRAPH 2003 Papers
3D motion retrieval with motion index tree

Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Automated extraction and parameterization of motions in large data sets

ACM SIGGRAPH 2004 Papers
On-line motion blending for real-time locomotion generation: Research Articles

Computer Animation and Virtual Worlds - Special Issue: The Very Best Papers from CASA 2004
Efficient content-based retrieval of motion capture data

ACM SIGGRAPH 2005 Papers
Motion modeling for on-line locomotion synthesis

Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Indexing large human-motion databases

VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Content-based retrieval for human motion data

Journal of Visual Communication and Image Representation

3D human motion retrieval based on ISOMAP dimension reduction

AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a framework for efficient content-based motion retrieval. To bridge the gap between user's vague perception and explicit motion scene description, we propose a Scene Description Language that can translate user's input into a series of set operations between inverted lists. Our Scene Description Language has three-layer structures, each describing scenes at different levels of granularity. By introducing automatic transition strategy into our retrieval process, our system can search motions that do not exist in a motion database. This property makes our system have potentials to serve as motion synthesis purpose. Moreover, by using various kinds of qualitative features and adaptive segments of motion capture data stream, we obtain a robust clustering that is flexible and efficient for constructing motion graph. Some experimental examples are given to demonstrate the effectiveness and efficiency of proposed algorithms.