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Intuitive Interactive Human-Character Posing with Millions of Example Poses
July/August 2011 (vol. 31 no. 4)
pp. 78-88
Xiaolin K. Wei, Texas A&M University
Jinxiang Chai, Texas A&M University
The authors present a data-driven algorithm for interactive 3D human-character posing. They formulate the problem in a maximum a posteriori (MAP) framework by combining the user's inputs with the priors embedded in prerecorded human poses. Maximizing the posterior probability lets them generate a most-likely human pose that satisfies the user constraints. The system can learn priors from a huge, heterogeneous human-motion-capture database (2.8 million prerecorded poses) and use them to generate a wide range of natural poses. No previous data-driven character-posing system has demonstrated this capability. In addition, the authors present two intuitive interfaces for interactive human-character posing: direct-manipulation interfaces and sketching interfaces. They show their system's superiority compared to standard inverse-kinematics techniques and alternative data-driven techniques.

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Index Terms:
computer graphics, data-driven character posing, inverse kinematics, motion-capture database, nonlinear optimization, statistical models, interactive techniques, graphics and multimedia
Citation:
Xiaolin K. Wei, Jinxiang Chai, "Intuitive Interactive Human-Character Posing with Millions of Example Poses," IEEE Computer Graphics and Applications, vol. 31, no. 4, pp. 78-88, July-Aug. 2011, doi:10.1109/MCG.2009.132
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