Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Generative modeling for continuous non-linearly embedded visual inference
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Automated extraction and parameterization of motions in large data sets
ACM SIGGRAPH 2004 Papers
Efficient content-based retrieval of motion capture data
ACM SIGGRAPH 2005 Papers
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Motion templates for automatic classification and retrieval of motion capture data
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
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
Information Retrieval for Music and Motion
Information Retrieval for Music and Motion
Human Motion: Understanding, Modelling, Capture, and Animation (Computational Imaging and Vision)
Human Motion: Understanding, Modelling, Capture, and Animation (Computational Imaging and Vision)
Nonparametric density estimation with adaptive, anisotropic kernels for human motion tracking
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
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This article presents a genetic learning algorithm to derive discrete patterns that can be used for classification and retrieval of 3D motion capture data. Based on boolean motion features, the idea is to learn motion class patterns in an evolutionary process with the objective to discriminate a given set of positive from a given set of negative training motions. Here, the fitness of a pattern is measured with respect to precision and recall in a retrieval scenario, where the pattern is used as a motion query. Our experiments show that motion class patterns can automate query specification without loss of retrieval quality.