Neural networks for pattern recognition
Neural networks for pattern recognition
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Motion texture: a two-level statistical model for character motion synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive motion generation from examples
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive control of avatars animated with human motion data
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Verbs and Adverbs: Multidimensional Motion Interpolation
IEEE Computer Graphics and Applications
Snap-together motion: assembling run-time animations
I3D '03 Proceedings of the 2003 symposium on Interactive 3D graphics
Realistic synthesis of novel human movements from a database of motion capture examples
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Flexible automatic motion blending with registration curves
Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation
Motion synthesis from annotations
ACM SIGGRAPH 2003 Papers
Style-based inverse kinematics
ACM SIGGRAPH 2004 Papers
Automated extraction and parameterization of motions in large data sets
ACM SIGGRAPH 2004 Papers
Performance animation from low-dimensional control signals
ACM SIGGRAPH 2005 Papers
Geostatistical motion interpolation
ACM SIGGRAPH 2005 Papers
Style translation for human motion
ACM SIGGRAPH 2005 Papers
Behavior planning for character animation
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
Fat graphs: constructing an interactive character with continuous controls
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
Proceedings of the 2007 symposium on Interactive 3D graphics and games
Near-optimal character animation with continuous control
ACM SIGGRAPH 2007 papers
Constraint-based motion optimization using a statistical dynamic model
ACM SIGGRAPH 2007 papers
Construction and optimal search of interpolated motion graphs
ACM SIGGRAPH 2007 papers
Interaction patches for multi-character animation
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
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Interactive generation of human animation with deformable motion models
ACM Transactions on Graphics (TOG)
Motion fields for interactive character locomotion
ACM SIGGRAPH Asia 2010 papers
Physically valid statistical models for human motion generation
ACM Transactions on Graphics (TOG)
Reconstructing whole-body motions with wrist trajectories
Graphical Models
Hybrid motion graph for character motion synthesis
Journal of Visual Languages and Computing
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This paper introduces a new generative statistical model that allows for human motion analysis and synthesis at both semantic and kinematic levels. Our key idea is to decouple complex variations of human movements into finite structural variations and continuous style variations and encode them with a concatenation of morphable functional models. This allows us to model not only a rich repertoire of behaviors but also an infinite number of style variations within the same action. Our models are appealing for motion analysis and synthesis because they are highly structured, contact aware, and semantic embedding. We have constructed a compact generative motion model from a huge and heterogeneous motion database (about two hours mocap data and more than 15 different actions). We have demonstrated the power and effectiveness of our models by exploring a wide variety of applications, ranging from automatic motion segmentation, recognition, and annotation, and online/offline motion synthesis at both kinematics and behavior levels to semantic motion editing. We show the superiority of our model by comparing it with alternative methods.