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Aaron Hertzmann


Wednesday 13th July 2011

11am - Noon


Malet Place Eng 1.02



Principles of Humanoid Locomotion Control


Understanding the control forces that drive humans and animals is fundamental to describing their movement. Good models of control would be informative for many fields. Although physics-based methods hold promise for creating animation, they have long been considered too difficult to design and control. Likewise, physical motion models, if developed, could be very valuable to human pose tracking and recognition in computer vision.

I will outline the main problems of human motion modeling, and describe some principles of humanoid motion from the biomechanics literature. Based on these principles, I will then present a new approach to control of physics-based characters based on high-level features of human movement.

These controllers provide unprecedented flexibility and generality in real-time character control: they capture many natural properties of human movement, they can be easily modified and applied to new characters, and they can handle a variety of different terrains and tasks, all within a single control strategy.

Until very recently, even making a controller walk without falling down was extraordinarily difficult. This is no longer the case. Our work, together with other recent results in this area, suggests that we are now ready to make great strides in locomotion.


Aaron Hertzmann, Associate Professor at the University of Toronto Computer Science Department has worked at Pixar Animation Studios, University of Washington, Microsoft Research, Mitsubishi Electric Research Lab, Interval
Research Corporation and NEC Research Institute. His awards include the MIT TR100, an Ontario Early Researcher Award, a Sloan Foundation Fellowship, a Microsoft New Faculty Fellowship, a UofT CS Teaching Award, the CACS/AIC Outstanding Young CS Researcher Award, and the Steacie Prize for Natural Sciences. He received a BA in Computer Science and Art & Art History from Rice University, and a Ph.D. in Computer Science from New
York University.