Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning variable-length Markov models of behavior
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Flexible automatic motion blending with registration curves
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Recognition of two-person interactions using a hierarchical Bayesian network
IWVS '03 First ACM SIGMM international workshop on Video surveillance
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Semantic Segmentation of Motion Capture Using Laban Movement Analysis
IVA '07 Proceedings of the 7th international conference on Intelligent Virtual Agents
Behavior histograms for action recognition and human detection
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
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We propose motion manifold learning and motion primitive segmentation framework for human motion synthesis from motion-captured data. High dimensional motion capture date are represented using a low dimensional representation by topology preserving network, which maps similar motion instances to the neighborhood points on the low dimensional motion manifold. Nonlinear manifold learning between a low dimensional manifold representation and high dimensional motion data provides a generative model to synthesize new motion sequence by controlling trajectory on the low dimensional motion manifold. We segment motion primitives by analyzing low dimensional representation of body poses through motion from motion captured data. Clustering techniques like k-means algorithms are used to find motion primitives after dimensionality reduction. Motion dynamics in training sequences can be described by transition characteristics of motion primitives. The transition matrix represents the temporal dynamics of the motion with Markovian assumption. We can generate new motion sequences by perturbing the temporal dynamics