Simulating biped behaviors from human motion data
ACM SIGGRAPH 2007 papers
Technical Section: Variational Bayesian noise estimation of point sets
Computers and Graphics
Spacetime expression cloning for blendshapes
ACM Transactions on Graphics (TOG)
Tones for real: Managing multipath in underwater acoustic wakeup
ACM Transactions on Sensor Networks (TOSN)
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Sophisticated computer graphics applications require complex models of appearance, motion, natural phenomena, and even artistic style. Such models are often difficult or impossible to design by hand. Recent research demonstrates that, instead, we can "learn" a dynamical and/or appearance model from captured data, and then synthesize realistic new data from the model. For example, we can capture the motions of a human actor and then generate new motions as they might be performed by that actor. Bayesian reasoning is a fundamental tool of machine learning and statistics, and it provides powerful tools for solving otherwise-difficult problems of learning about the world from data. Beginning from first principles, this course develops the general methodologies for designing learning algorithms and describes their application to several problems in graphics.