Shaking hands in latent space modeling emotional interactions with Gaussian process latent variable models

Authors:
Nick Taubert;Dominik Endres;Andrea Christensen;Martin A. Giese
Affiliations:
Dept. of Cognitive Neurology, University Clinic, CIN, HIH and University of Tübingen, Tübingen, Germany;Dept. of Cognitive Neurology, University Clinic, CIN, HIH and University of Tübingen, Tübingen, Germany;Dept. of Cognitive Neurology, University Clinic, CIN, HIH and University of Tübingen, Tübingen, Germany;Dept. of Cognitive Neurology, University Clinic, CIN, HIH and University of Tübingen, Tübingen, Germany
Venue:
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Year:
2011

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Abstract

We present an approach for the generative modeling of human interactions with emotional style variations. We employ a hierarchical Gaussian process latent variable model (GP-LVM) to map motion capture data of handshakes into a space of low dimensionality. The dynamics of the handshakes in this low dimensional space are then learned by a standard hidden Markov model, which also encodes the emotional style variation. To assess the quality of generated and rendered handshakes, we asked human observers to rate them for realism and emotional content. We found that generated and natural handshakes are virtually indistinguishable, proving the accuracy of the learned generative model.