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Learning Finite-State Machine Controllers From Motion Capture Data

机译:从运动捕捉数据中学习有限状态机控制器

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With characters in computer games and interactive media increasingly being based on real actors, the individuality of an actor's performance should not only be reflected in the appearance and animation of the character but also in the AI that governs the character's behavior and interactions with the environment. Machine learning methods applied to motion capture data provide a way of doing this. This paper presents a method for learning the parameters of a finite-state machine (FSM) controller. The method learns both the transition probabilities of the FSM and also how to select animations based on the current state.
机译:随着计算机游戏和互动媒体中的角色越来越多地基于真实的演员,演员的表演不仅应体现在角色的外观和动画上,还应体现在控制角色行为以及与环境互动的AI中。应用于运动捕获数据的机器学习方法提供了一种方法。本文提出了一种用于学习有限状态机(FSM)控制器参数的方法。该方法不仅学习FSM的过渡概率,还学习如何基于当前状态选择动画。

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