Motion Adaptation with Cascaded Inequality Tasks

Proceedings of ACM SIGGRAPH Conference on Motion, Interaction and Games 2019, pp.30:1-30:10, 2019.

Authors: Tomohiko Mukai, Shigeru Kuriyama, Masaki Oshita

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Abstract: A clip of character motion can be adapted to a change in environment or to another character of a different body size via a numerical optimization with several tasks including the objective of movement and physical constraints. Conventional methods, however, lack the design flexibility of such adaptation tasks because of the simple problem formulation. We propose a motion adaptation framework based on a cascaded series of quadratic programs. Our system introduces a layered structure of strictly prioritized tasks, each layer of which comprises arbitrary types of equality and inequality tasks. The cascaded solver identifies the optimal solution in each layer without affecting the fulfillment of the higher layer tasks. The stable computation of the cascaded optimization supports the intuitive design of the spacetime tasks even for novice users. The capability of our method was demonstrated through several experiments of motion adaptation with prioritized inequality tasks, such as environmental adaptation and adaptation of interactive behavior between two characters.

Acknowledgements: This work was supported by JSPS KAKENHI Grant Number 15K16110 and 15H02704.

©ACM 2019. This is the author’s version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM SIGGRAPH Conference on Motion, Interaction and Games 2019,

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  1. Pingback: 2019年度のまとめ – Mukai Laboratory

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