Style-based inverse kinematics

• Keith Grochow

  University of Washington

• Steven L. Martin

  University of Washington

• Aaron Hertzmann

  University of Toronto

• Zoran Popović

  University of Washington

抄録

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.

収録刊行物

• ACM Transactions on Graphics

  ACM Transactions on Graphics 23 (3), 522-531, 2004-08

  Association for Computing Machinery (ACM)

キーワード

• Computer Graphics and Computer-Aided Design

詳細情報

• CRID: 1362262944072382208
• NII論文ID: 30022063549
• DOI: 10.1145/1015706.1015755
• ISSN: 15577368
• ISSN: 07300301
• Web Site: https://dl.acm.org/doi/pdf/10.1145/1015706.1015755
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  • CiNii Articles