Techniques for Assessing Polygonal Approximations of Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer animation: algorithms and techniques
Computer animation: algorithms and techniques
Interactive control of avatars animated with human motion data
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Evaluation of key frame-based retrieval techniques for video
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
3D motion retrieval with motion index tree
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
Performance timing for keyframe animation
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
Example-based motion cloning: 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
Content-based retrieval for human motion data
Journal of Visual Communication and Image Representation
LoD-based locomotion engine for game characters
Edutainment'07 Proceedings of the 2nd international conference on Technologies for e-learning and digital entertainment
An efficient approach for human motion data mining based on curves matching
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Efficient motion retrieval in large motion databases
Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games
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This paper proposes a keyframe extraction method based on a novel layered curve simplification algorithm for motion capture data. Bone angles are employed as motion features and keyframe candidates can be selected based on them. After that, the layered curve simplification algorithm will be used to refine those candidates and the keyframe collection can be gained. To meet different requirements for compression and level of detail of motion abstraction, adaptive extraction parameters are also applied. The experiments demonstrate that our method can not only compress and summarize the motion capture data efficiently, but also keep the consistency of keyframe collection between similar human motion sequences, which is of great benefit to further motion data retrieval or editing.