Paper Info
Title
Classification and translation of style and affect in human motion using RBF neural networks
Abstract
Human motion can be carried out with a variety of different affects or styles such as happy, sad, energetic, and tired among many others. Modeling and classifying these styles, and more importantly, translating them from one sequence onto another has become a popular problem in the fields of graphics, multimedia, and human computer interaction. In this paper, radial basis functions (RBF) are used to model and extract stylistic and affective features from motion data. We demonstrate that using only a few basis functions per degree of freedom, successful modeling of styles in cycles of human walk can be achieved. Furthermore, we employ an ensemble of RBF neural networks to learn the affective/stylistic features following time warping and principal component analysis. The system learns the components and classifies stylistic motion sequences into distinct affective and stylistic classes. The system also utilizes the ensemble of neural networks to learn motion affects and styles such that it can translate them onto neutral input sequences. Experimental results along with both numerical and perceptual validations confirm the highly accurate and effective performance of the system.
Year
DOI
Venue
2014
10.1016/j.neucom.2013.09.001
Neurocomputing
Keywords
Field
DocType
rbf neural network,human motion,motion data,distinct affective,stylistic feature,stylistic motion sequence,stylistic class,human walk,human computer interaction,affective feature,motion capture,classification,radial basis functions,neural networks
Graphics,Motion capture,Radial basis function,Dynamic time warping,Pattern recognition,Computer science,Basis function,Artificial intelligence,Artificial neural network,Perception,Machine learning,Principal component analysis
Journal
Volume
ISSN
Citations 
129,
0925-2312
12
PageRank 
References 
Authors
0.50
36
2
Name
Order
Citations
PageRank
S. Ali Etemad1554.94
Ali Arya211020.31