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Style-based inverse kinematics

Published: 01 August 2004 Publication History

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 real-time. 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 posing from a 2D image.

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References

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cover image ACM Conferences
SIGGRAPH '04: ACM SIGGRAPH 2004 Papers
August 2004
684 pages
ISBN:9781450378239
DOI:10.1145/1186562
  • Editor:
  • Joe Marks
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 August 2004

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Author Tags

  1. Character animation
  2. Gaussian Processes
  3. Inverse Kinematics
  4. machine learning
  5. motion style
  6. non-linear dimensionality reduction
  7. style interpolation

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SIGGRAPH '04 Paper Acceptance Rate 83 of 478 submissions, 17%;
Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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