Motion texture: a two-level statistical model for character motion synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Automated Derivation of Primitives for Movement Classification
Autonomous Robots
A Motion Recognition Method by Using Primitive Motions
VDB 5 Proceedings of the Fifth Working Conference on Visual Database Systems: Advances in Visual Information Management
Unsupervised Analysis of Human Gestures
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Automated derivation of behavior vocabularies for autonomous humanoid motion
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
A spatio-temporal extension to Isomap nonlinear dimension reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Acquiring and validating motion qualities from live limb gestures
Graphical Models
Automated gesture segmentation from dance sequences
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Human motion synthesis by motion manifold learning and motion primitive segmentation
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Gesture-Based Human-Computer Interaction and Simulation
EMVIZ: the poetics of movement quality visualization
Proceedings of the International Symposium on Computational Aesthetics in Graphics, Visualization, and Imaging
Efficient motion retrieval in large motion databases
Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games
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Many applications that utilize motion capture data require small, discrete, semantic segments of data, but most motion capture collection processes produce long sequences of data. The smaller segments are often created from the longer sequences manually. This segmentation process is very laborious and time consuming. This paper presents an automatic motion capture segmentation method based on movement qualities derived from Laban Movement Analysis (LMA). LMA provides a good compromise between high-level semantic features, which are difficult to extract for general motions, and low-level kinematic features which, often yield unsophisticated segmentations. The LMA features are computed using a collection of neural networks trained with temporal variance in order to create a classifier that is more robust with regard to input boundaries. The actual segmentation points are derived through simple time series analysis of the LMA features.