# Brickify: Enabling Expressive Design Intent Specification through Direct Manipulation on Design Tokens

arXiv: https://arxiv.org/abs/2502.21219 · HTML: https://mlsanity.com/paper/2502.21219/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2025.

## Built on by (most cited first, 8 of 8)

- [StoryEnsemble: Enabling Dynamic Exploration & Iteration in the Design Process with AI and Forward-Backward Propagation](https://mlsanity.com/paper/2508.03182/) (2025)
- [DesignerlyLoop: Forming Design Intent through Curated Reasoning for Human-LLM Alignment](https://mlsanity.com/paper/2511.15331/) (2025)
- [AnimAgents: Coordinating Multi-Stage Animation Pre-Production with Human-Multi-Agent Collaboration](https://mlsanity.com/paper/2511.17906/) (2025)
- ["I Know It When I See It": Mood Spaces for Connecting and Expressing Visual Concepts](https://mlsanity.com/paper/2504.15145/) (2025)
- [WordCraft: Scaffolding the Keyword Method for L2 Vocabulary Learning with Multimodal LLMs](https://mlsanity.com/paper/2602.00762/) (2026)
- [Adaptive Prompt Elicitation for Text-to-Image Generation](https://mlsanity.com/paper/2602.04713/) (2026)
- [Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs](https://mlsanity.com/paper/2602.06197/) (2026)
- [Alignment Games: A Framework for Conceptual Repair in Human-AI Collaboration](https://mlsanity.com/paper/2609.35197/) (2026)

## Abstract (arXiv)

Expressing design intent using natural language prompts requires designers to verbalize the ambiguous visual details concisely, which can be challenging or even impossible. To address this, we introduce Brickify, a visual-centric interaction paradigm -- expressing design intent through direct manipulation on design tokens. Brickify extracts visual elements (e.g., subject, style, and color) from reference images and converts them into interactive and reusable design tokens that can be directly manipulated (e.g., resize, group, link, etc.) to form the visual lexicon. The lexicon reflects users' intent for both what visual elements are desired and how to construct them into a whole. We developed Brickify to demonstrate how AI models can interpret and execute the visual lexicon through an end-to-end pipeline. In a user study, experienced designers found Brickify more efficient and intuitive than text-based prompts, allowing them to describe visual details, explore alternatives, and refine complex designs with greater ease and control.