SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Multiresolution sampling procedure for analysis and synthesis of texture images
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive control of avatars animated with human motion data
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Motion capture assisted animation: texturing and synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Verbs and Adverbs: Multidimensional Motion Interpolation
IEEE Computer Graphics and Applications
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Learning physics-based motion style with nonlinear inverse optimization
ACM SIGGRAPH 2005 Papers
Style translation for human motion
ACM SIGGRAPH 2005 Papers
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Unsupervised Discovery of Action Classes
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Guided time warping for motion editing
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
A simple footskate removal method for virtual reality applications
The Visual Computer: International Journal of Computer Graphics
Local velocity-adapted motion events for spatio-temporal recognition
Computer Vision and Image Understanding
Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of affect based on gait patterns
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Modeling style and variation in human motion
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Natural Character Posing from a Large Motion Database
IEEE Computer Graphics and Applications
Human attributes from 3D pose tracking
Computer Vision and Image Understanding
Learning and Matching of Dynamic Shape Manifolds for Human Action Recognition
IEEE Transactions on Image Processing
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
A Customizable Time Warping Method for Motion Alignment
ICSC '13 Proceedings of the 2013 IEEE Seventh International Conference on Semantic Computing
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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.