Evaluating the plausibility of edited throwing animations

Animation budget constraints during the development of a game often call for the use of a limited set of generic motions. Editing operations are thus generally required to animate virtual characters with a sufficient level of variety. Evaluating the perceptual plausibility of edited animations can therefore contribute greatly towards producing visually plausible animations. In this paper we study observers' sensitivity to manipulations of overarm and underarm biological throwing animations. In our first experiment, we used Dynamic Time Warping to edit the biological throwing motions, and modified the release velocity of the ball accordingly. We found that observers are more tolerant to speeding up of the original throwing motion than to slowing down, and that slowed down underarm throws are perceived as particularly unnatural. In our second experiment, we modified separately horizontal and vertical components of the release velocity of the ball, while leaving the motion of the thrower unchanged. We found that observers are more sensitive to manipulations of the horizontal component in overarm throws, and of the vertical component in underarm throws. As in the first experiment, we found that observers are most disturbed by decreases in the velocity of the ball in underarm throws. Our results provide valuable insights for developers of games and VR applications by specifying thresholds for the perceptual plausibility of throwing manipulations

Evaluating the plausibility of edited throwing animations

Animation budget constraints during the development of a game often call for the use of a limited set of generic motions. Editing operations are thus generally required to animate virtual characters with a sufficient level of variety. Evaluating the perceptual plausibility of edited animations can therefore contribute greatly towards producing visually plausible animations. In this paper we study observers\u2019 sensitivity to manipulations of overarm and underarm biological throwing animations. In our first experiment, we used Dynamic Time Warping to edit the biological throwing motions, and modified the release velocity of the ball accordingly. We found that observers are more tolerant to speeding up of the original throwing motion than to slowing down, and that slowed down underarm throws are perceived as particularly unnatural. In our second experiment, we modified separately horizontal and vertical components of the release velocity of the ball, while leaving the motion of the thrower unchanged. We found that observers are more sensitive to manipulations of the horizontal component of velocity in overarm throws, and of the vertical component in underarm throws. As in the first experiment, we found that observers are most disturbed by decreases in the velocity of the ball in underarm throws. Our results provide valuable insights for developers of games and VR applications by specifying thresholds for the perceptual plausibility of manipulations of throwing actions

Natural preparation behaviour synthesis

Humans adjust their movements in advance to prepare for the forthcoming action, resulting in efficient and smooth transitions. However, traditional computer animation approaches such as motion graphs simply concatenate a series of actions without taking into account the following one. In this paper, we propose a new method to produce preparation behaviors using reinforcement learning. As an offline process, the system learns the optimal way to approach a target and to prepare for interaction. A scalar value called the level of preparation is introduced, which represents the degree of transition from the initial action to the interacting action. To synthesize the movements of preparation, we propose a customized motion blending scheme based on the level of preparation, which is followed by an optimization framework that adjusts the posture to keep the balance. During runtime, the trained controller drives the character to move to a target with the appropriate level of preparation, resulting in a humanlike behavior. We create scenes in which the character has to move in a complex environment and to interact with objects, such as crawling under and jumping over obstacles while walking. The method is useful not only for computer animation but also for real-time applications such as computer games, in which the characters need to accomplish a series of tasks in a given environment

Preparation behaviour synthesis with reinforcement learning

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