A Rotation Invariant Latent Factor Model for Moveme Discovery from Static Poses

We tackle the problem of learning a rotation invariant latent factor model when the training data is comprised of lower-dimensional projections of the original feature space. The main goal is the discovery of a set of 3-D bases poses that can characterize the manifold of primitive human motions, or...

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Bibliographic Details
Published in2016 IEEE 16th International Conference on Data Mining (ICDM) pp. 1179 - 1184
Main Authors Ronchi, Matteo Ruggero, Joon Sik Kim, Yisong Yue
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
Activity recognition
Data models
Human Motion Analysis
Image reconstruction
Latent Factor Modeling
Machine Learning
Matrix decomposition
Representation Learning
Rotation Invariance
Solid modeling
Training
Training data
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Summary:We tackle the problem of learning a rotation invariant latent factor model when the training data is comprised of lower-dimensional projections of the original feature space. The main goal is the discovery of a set of 3-D bases poses that can characterize the manifold of primitive human motions, or movemes, from a training set of 2-D projected poses obtained from still images taken at various camera angles. The proposed technique for basis discovery is data-driven rather than hand-designed. The learned representation is rotation invariant, and can reconstruct any training instance from multiple viewing angles. We apply our method to modeling human poses in sports (via the Leeds Sports Dataset), and demonstrate the effectiveness of the learned bases in a range of applications such as activity classification, inference of dynamics from a single frame, and synthetic representation of movements.
ISSN:2374-8486
DOI:10.1109/ICDM.2016.0156