Conference 5–9 August 2012
Exhibition 7–9 August 2012
Los Angeles Convention Center

Character Locomotion

Technical Papers

Character Locomotion

Monday, 6 August 9:00 AM - 10:30 AM | Los Angeles Convention Center, Room 515AB
Session Chair: Jehee Lee, Seoul National University

Optimizing Locomotion Controllers Using Biologically Based Actuators and Objectives

Synthesis of walking and running controllers for physically simulated 3D humanoids with sagittal hip, knee, and ankle joint degrees of freedom actuated using musculotendon models with biologically motivated control laws. Using biologically based actuators and objectives for optimization measurably increases the realism of gaits generated by locomotion controllers.

Jack M. Wang
Stanford University

Samuel R. Hamner
Stanford University

Scott L. Delp
Stanford University

Vladlen Koltun
Stanford University

Soft Body Locomotion

A physically based system to simulate and control the locomotion of soft-body characters without skeletons, including walking, jumping, crawling, rolling, and balancing.

Jie Tan
Georgia Institute of Technology

Greg Turk
Georgia Institute of Technology

C. Karen Liu
Georgia Institute of Technology

Video-Based 3D Motion Capture Through Biped Control

This paper estimates biped control from monocular video by implicitly recovering physically realistic three-dimensional motion of a subject along with a responsive character model (controller) capable of replaying this motion in other environments and under physical perturbations.

Marek Vondrak
Brown University

Leonid Sigal
Disney Research Pittsburgh

Jessica Hodgins
Disney Research Pittsburgh and Carnegie Mellon University

Odest Jenkins
Brown University

Continuous Character Control With Low-Dimensional Embeddings

This paper presents a technique that animates characters performing user-specified tasks by learning a low-dimensional space of appropriate character poses. By controlling the character through a reduced space, the method can discover new transitions and tractably precompute a near-optimal control policy.

Sergey Levine
Stanford University

Jack M. Wang
Stanford University

Alexis Haraux
Stanford University

Zoran Popović
University of Washington

Vladlen Koltun
Stanford University