Technical Papers

Human Modeling

Thursday, 29 July | 2:00 PM - 3:30 PM | Room 502 B
Session Chair: Jessica Hodgins, Carnegie Mellon University
Learning Behavior Styles With Inverse Reinforcement Learning

A method for inferring the behavior styles of character controllers from examples. This paper shows that a rich set of behavior variations can be captured by determining the appropriate reward function in the reinforcement-learning framework and that the discovered-reward function can be applied to different tasks.

Seong Jae Lee
University of Washington

Zoran Popović
University of Washington

A Synthetic-Vision-Based Steering Approach for Crowd Simulation

Humans control their locomotion from their optic flow. This paper simulates crowds of individual walkers steered from their synthetic vision. The avoidance model is inspired by cognitive science work on human locomotion. The simulations display emerging self-organized patterns at a global scale from the local interactions between walkers.

Jan Ondrej
INRIA Rennes

Julien Pettre
INRIA Rennes

Anne-Helene Olivier
INRIA Rennes

Stephane Donikian
INRIA Rennes

Comprehensive Biomechanical Modeling and Simulation of the Upper Body

This comprehensive biomechanical model of the human upper body confronts the combined challenge of modeling and controlling more or less all of the relevant articular bones and muscles as well as simulating the physics-based deformations of the soft tissues.

Sung-Hee Lee
University of California, Los Angeles

Eftychios Sifakis
University of California, Los Angeles

Demetri Terzopoulos
University of California, Los Angeles

Gesture Controllers

A method for generating body-language animations from live speech using optimal policy controllers. The method is modular and versatile, admitting a variety of applications and additional input channels.

Sergey Levine
Stanford University

Philipp Krähenbühl
Stanford University

Sebastian Thrun
Stanford University

Vladlen Koltun
Stanford University

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