# A Collaborative, Interactive and Context-Aware Drawing Agent for Co-Creative Design

arXiv: https://arxiv.org/abs/2209.12588 · HTML: https://mlsanity.com/paper/2209.12588/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2022.

## Built on by (most cited first, 10 of 10)

- [SketchAgent: Language-Driven Sequential Sketch Generation](https://mlsanity.com/paper/2411.17673/) (2024)
- [Creativity in LLM-based Multi-Agent Systems: A Survey](https://mlsanity.com/paper/2505.21116/) (2025)
- [Measuring Diversity in Co-creative Image Generation](https://mlsanity.com/paper/2403.13826/) (2024)
- [A Systematic Review of Human-AI Co-Creativity](https://mlsanity.com/paper/2506.21333/) (2025)
- [Stroke of Surprise: Progressive Semantic Illusions in Vector Sketching](https://mlsanity.com/paper/2602.12280/) (2026)
- [Affect-Conditioned Image Generation](https://mlsanity.com/paper/2302.09742/) (2023)
- [HOTS3D: Hyper-Spherical Optimal Transport for Semantic Alignment of Text-to-3D Generation](https://mlsanity.com/paper/2407.14419/) (2024)
- [Real-Time Intuitive AI Drawing System for Collaboration: Enhancing Human Creativity through Formal and Contextual Intent Integration](https://mlsanity.com/paper/2508.19254/) (2025)
- [Data-Efficient Brushstroke Generation with Diffusion Models for Oil Painting](https://mlsanity.com/paper/2603.01103/) (2026)
- [The Image Reconstruction Game: Drawing Common Ground Through Iterative Multimodal Dialogue](https://mlsanity.com/paper/2606.01901/) (2026)

## Abstract (arXiv)

Recent advances in text-conditioned generative models have provided us with neural networks capable of creating images of astonishing quality, be they realistic, abstract, or even creative. These models have in common that (more or less explicitly) they all aim to produce a high-quality one-off output given certain conditions, and in that they are not well suited for a creative collaboration framework. Drawing on theories from cognitive science that model how professional designers and artists think, we argue how this setting differs from the former and introduce CICADA: a Collaborative, Interactive Context-Aware Drawing Agent. CICADA uses a vector-based synthesis-by-optimisation method to take a partial sketch (such as might be provided by a user) and develop it towards a goal by adding and/or sensibly modifying traces. Given that this topic has been scarcely explored, we also introduce a way to evaluate desired characteristics of a model in this context by means of proposing a diversity measure. CICADA is shown to produce sketches of quality comparable to a human user's, enhanced diversity and most importantly to be able to cope with change by continuing the sketch minding the user's contributions in a flexible manner.