Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Sampling plausible solutions to multi-body constraint problems
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Footskate cleanup for motion capture editing
Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation
Motion texture: a two-level statistical model for character motion synthesis
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
Real Time Responsive Animation with Personality
IEEE Transactions on Visualization and Computer Graphics
Interpolation Synthesis of Articulated Figure Motion
IEEE Computer Graphics and Applications
Verbs and Adverbs: Multidimensional Motion Interpolation
IEEE Computer Graphics and Applications
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Animating by Multi-Level Sampling
CA '00 Proceedings of the Computer Animation
A spatio-temporal extension to Isomap nonlinear dimension reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Style translation for human motion
ACM SIGGRAPH 2005 Papers
ACM SIGGRAPH 2004 Course Notes
Learning Bayesian Networks
A decision network framework for the behavioral animation of virtual humans
SCA '07 Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer animation
Constraint-based motion optimization using a statistical dynamic model
ACM SIGGRAPH 2007 papers
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clone attack! Perception of crowd variety
ACM SIGGRAPH 2008 papers
Two-Character Motion Analysis and Synthesis
IEEE Transactions on Visualization and Computer Graphics
Generalizing motion edits with Gaussian processes
ACM Transactions on Graphics (TOG)
Unique Character Instances for Crowds
IEEE Computer Graphics and Applications
Sampling-based contact-rich motion control
ACM SIGGRAPH 2010 papers
Style-content separation by anisotropic part scales
ACM SIGGRAPH Asia 2010 papers
Modeling style and variation in human motion
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Physically valid statistical models for human motion generation
ACM Transactions on Graphics (TOG)
A style controller for generating virtual human behaviors
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Walk this way: a lightweight, data-driven walking synthesis algorithm
MIG'11 Proceedings of the 4th international conference on Motion in Games
Continuous character control with low-dimensional embeddings
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Proceedings of the ACM Symposium on Applied Perception
Motion graphs++: a compact generative model for semantic motion analysis and synthesis
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
One-to-many: example-based mesh animation synthesis
Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Diverse motion variations for physics-based character animation
Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Proceedings of the ACM Symposium on Applied Perception
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We present a novel method to model and synthesize variation in motion data. Given a few examples of a particular type of motion as input, we learn a generative model that is able to synthesize a family of spatial and temporal variants that are statistically similar to the input examples. The new variants retain the features of the original examples, but are not exact copies of them. We learn a Dynamic Bayesian Network model from the input examples that enables us to capture properties of conditional independence in the data, and model it using a multivariate probability distribution. We present results for a variety of human motion, and 2D handwritten characters. We perform a user study to show that our new variants are less repetitive than typical game and crowd simulation approaches of re-playing a small number of existing motion clips. Our technique can synthesize new variants efficiently and has a small memory requirement.