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This paper introduces fog, a function composition framework designed to generate and manipulate motion functions that express both motion and emotion in animations. By evaluating 452 fog-generated animations, the authors demonstrate that their framework achieves a recognition accuracy of 68%, significantly outperforming the chance baseline by 2.68 times. Additionally, user studies indicate that fog enhances the iterative design process for both professionals and novices, facilitating greater exploration and control in animation creation.
fog enables a dramatic leap in motion recognition accuracy, allowing users to intuitively express complex emotions and movements in animations.
Motion and emotion are core parts of intelligent, expressive behavior. In this paper, we introduce fog, a function composition framework for implementing and compose motion functions. We demonstrate how fog can be used to express motion and emotion in Heider-Simmel style animations. This code generation framework can help users generate functions for verbs, adverbs, gestures, and emotions to create an open-ended motion vocabulary. It is complemented by an animation editor that helps users refine motion through direct manipulation and dynamically generated UI. We evaluate our approach with a perceptual evaluation, where we test 452 fog-generated animations to see if people can recognize the semantic meaning of the motion. We find that fog's motion functions can be recognized at 68% accuracy, a 2.68x improvement over a chance baseline. In a mixed-methods user study with professionals and novices, we show that fog in interface form can support users with more rapid iteration, exploration, and control.
