SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Training Hidden Markov Models with Multiple Observations-A Combinatorial Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Verbs and Adverbs: Multidimensional Motion Interpolation
IEEE Computer Graphics and Applications
Interpolation Synthesis for Articulated Figure Motion
VRAIS '97 Proceedings of the 1997 Virtual Reality Annual International Symposium (VRAIS '97)
Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces
ACM SIGGRAPH 2004 Papers
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Performance animation from low-dimensional control signals
ACM SIGGRAPH 2005 Papers
Geostatistical motion interpolation
ACM SIGGRAPH 2005 Papers
Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
Hierarchical Gaussian process latent variable models
Proceedings of the 24th international conference on Machine learning
Multifactor Gaussian process models for style-content separation
Proceedings of the 24th international conference on Machine learning
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Animating responsive characters with dynamic constraints in near-unactuated coordinates
ACM SIGGRAPH Asia 2008 papers
Generalizing motion edits with Gaussian processes
ACM Transactions on Graphics (TOG)
Modeling spatial and temporal variation in motion data
ACM SIGGRAPH Asia 2009 papers
Continuous character control with low-dimensional embeddings
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Proceedings of the ACM Symposium on Applied Perception
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Building on our previous work [Taubert et al. 2012], we present an approach for real-time interaction between a real human and an avatar. We generate reactive motions by a dynamical extension of a hierarchical Gaussian process latent variable model, including latent dimensions for emotional style variation and target positions. This allows the avatar to produce accurate reactive motions to the human. To validate our approach, we developed a real-time application where an avatar and a human actor engage in emotional 'high fives'. Furthermore, we show preliminary results indicating that humans do perceive emotions more accurately when engaging in interaction as opposed to passive observation.