Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 7-Day Trial for You or Your Team.

Learn More →

Style-based inverse kinematics

Style-based inverse kinematics Style-Based Inverse Kinematics Keith Grochow1 Steven L. Martin1 1 University Aaron Hertzmann2 2 University Zoran Popovi 1 c of Washington of Toronto Abstract This paper presents an inverse kinematics system based on a learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraints, in realtime. Training the model on different input data leads to different styles of IK. The model is represented as a probability distribution over the space of all possible poses. This means that our IK system can generate any pose, but prefers poses that are most similar to the space of poses in the training data. We represent the probability with a novel model called a Scaled Gaussian Process Latent Variable Model. The parameters of the model are all learned automatically; no manual tuning is required for the learning component of the system. We additionally describe a novel procedure for interpolating between styles. Our style-based IK can replace conventional IK, wherever it is used in computer animation and computer vision. We demonstrate our system in the context of a number of applications: interactive character posing, trajectory keyframing, real-time motion capture with missing markers, and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Style-based inverse kinematics

Grochow, Keith; Martin, Steven L.; Hertzmann, Aaron; Popović, Zoran
Association for Computing Machinery — Aug 8, 2004
Download PDF
10 pages

Loading next page...
 
/lp/association-for-computing-machinery/style-based-inverse-kinematics-4aAvNIFdUA

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Datasource
Association for Computing Machinery
Copyright
Copyright © 2004 by ACM Inc.
doi
10.1145/1186562.1015755
Publisher site
See Article on Publisher Site

Abstract

Style-Based Inverse Kinematics Keith Grochow1 Steven L. Martin1 1 University Aaron Hertzmann2 2 University Zoran Popovi 1 c of Washington of Toronto Abstract This paper presents an inverse kinematics system based on a learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraints, in realtime. Training the model on different input data leads to different styles of IK. The model is represented as a probability distribution over the space of all possible poses. This means that our IK system can generate any pose, but prefers poses that are most similar to the space of poses in the training data. We represent the probability with a novel model called a Scaled Gaussian Process Latent Variable Model. The parameters of the model are all learned automatically; no manual tuning is required for the learning component of the system. We additionally describe a novel procedure for interpolating between styles. Our style-based IK can replace conventional IK, wherever it is used in computer animation and computer vision. We demonstrate our system in the context of a number of applications: interactive character posing, trajectory keyframing, real-time motion capture with missing markers, and

There are no references for this article.