The EMOTE model for effort and shape
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Motion synthesis from annotations
ACM SIGGRAPH 2003 Papers
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
Automated extraction and parameterization of motions in large data sets
ACM SIGGRAPH 2004 Papers
Acquiring and validating motion qualities from live limb gestures
Graphical Models
A system for analyzing and indexing human-motion databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Efficient content-based retrieval of motion capture data
ACM SIGGRAPH 2005 Papers
GI '06 Proceedings of Graphics Interface 2006
Indexing large human-motion databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Perceptually consistent example-based human motion retrieval
Proceedings of the 2009 symposium on Interactive 3D graphics and games
Indexing and retrieval of human motion data by a hierarchical tree
Proceedings of the 16th ACM Symposium on Virtual Reality Software and Technology
Synthesis and editing of personalized stylistic human motion
Proceedings of the 2010 ACM SIGGRAPH symposium on Interactive 3D Graphics and Games
Fast local and global similarity searches in large motion capture databases
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
A Virtual Reality Dance Training System Using Motion Capture Technology
IEEE Transactions on Learning Technologies
Human Motion Retrieval from Hand-Drawn Sketch
IEEE Transactions on Visualization and Computer Graphics
Efficient motion retrieval in large motion databases
Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games
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Recently, there has been an increasing use of pre-recorded motion capture data, making motion indexing and classification essential for animating virtual characters and synthesising different actions. In this paper, we use a variety of features that encode characteristics of motion using the Body, Effort, Shape and Space components of Laban Movement Analysis (LMA), to explore the motion quality from acted dance performances. Using Principal Component Analysis (PCA), we evaluate the importance of the proposed features - with regards to their ability to separate the performer's emotional state - indicating the weight of each feature in motion classification. PCA has been also used for dimensionality reduction, laying the foundation for the qualitative and quantitative classification of movements based on their LMA characteristics. Early results show that the proposed features provide a representative space for indexing and classification of dance movements with regards to the emotion, which can be used for synthesis and composition purposes.