자동 타임 워핑에 기반한 온라인 궤적 최적화
On-line Trajectory Optimization Based on Automatic Time Warping
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초록
본 논문 에서는 물리 기반 가상 환경에서 참조 동작을 추적하는캐릭터 동작을 생성 할 때 캐릭터 동작에 최적화와 함께 참조 동작 에 대한 타임 워핑 (time warping)을 동시에 수행할 수 있는 새로운 온라인 궤적 최적화 (trajectory optimization) 기법을 제안한다. 일반 적으로 참조 동작에 대한 샘플링 시간이 균일한 간격으로 고정되어 있는 기존의 물리 기반 캐릭터 애니메이션 기법과는 달리 , 본 논문에서 제안하는 방법은 캐릭터 동작의 물리적 변화와 함께 샘플링 시간의 변화를 동시에 최적화 시킴으로써 외력에 대해 더욱 효과적으로 대응할 수 있는 참조 동작에 대한 최적의 타임 워핑을 찾아낸다 . 이를 위해 , 전신 캐릭터 (full -body character)의 동역학과 함께 참조 동작에 대한 샘플링 시간의 변화를 함께 고려한 최적 제어 문제 (optimal control problem)를 정형화하고 이 문제를 실행 시간에 시간축을 따라 이동하는 고정된 크기의 시간 윈도우에 대해 반복적 으로 풂으로써 캐릭터 동작과 샘플링 시간에 대한 최적 제어 정책 (optimal control policy)을 생성하는 생성하는 모델예측제어(model predictive control) 프레임워크를 제안한다. 실험을 통해 , 제안된 프레임워크가 하나의 참조 동작만으로 외력에 대해 강인하게 반응하는 동작을 생성하 , 배경 음악에 따라 리드미컬한 동작을 생성하는데 효과적임을 보여준다.
Abstract
This paper presents a novel on-line trajectory optimization framework based on automatic time warping, which performs the time warping of a reference motion while optimizing character motion control. Unlike existing physics-based character animation methods where sampling times for a reference motion are uniform or fixed during optimization in general, our method considers the change of sampling times on top of the dynamics of character motion in the same optimization, which allows the character to effectively respond to external pushes with optimal time warping. In order to do so, we formulate an optimal control problem which takes into account both the full-body dynamics and the change of sampling time for a reference motion, and present a model predictive control framework that produces an optimal control policy for character motion and sampling time by repeatedly solving the problem for a fixed-span time window while shifting it along the time axis. Our experimental results show the robustness of our framework to external perturbations and the effectiveness on rhythmic motion synthesis in accordance with a given piece of background music.
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