# fog: Expressing Motion and Emotion through Function Composition of AI-Generated Code

arXiv: https://arxiv.org/abs/2607.07952 · HTML: https://mlsanity.com/paper/2607.07952/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 8 Jul 2026.

## Builds on (most cited first, 3 of 3)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [LogoMotion: Visually-Grounded Code Synthesis for Creating and Editing Animation](https://mlsanity.com/paper/2405.07065/) (2024)
- [Toyteller: AI-powered Visual Storytelling Through Toy-Playing with Character Symbols](https://mlsanity.com/paper/2501.13284/) (2025)

## Abstract (arXiv)

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.