Brickify: Enabling Expressive Design Intent Specification through Direct Manipulation on Design Tokens
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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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Feb 2025.
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Citing papers per year: 2025: 4; 2026: 4.
8 papers.
- 2025StoryEnsemble: Enabling Dynamic Exploration & Iteration in the Design Process with AI and Forward-Backward Propagationcited by 2
- 2025DesignerlyLoop: Forming Design Intent through Curated Reasoning for Human-LLM Alignmentcited by 1
- 2025AnimAgents: Coordinating Multi-Stage Animation Pre-Production with Human-Multi-Agent Collaborationcited by 1
- 2025"I Know It When I See It": Mood Spaces for Connecting and Expressing Visual Conceptscited by 0
- 2026WordCraft: Scaffolding the Keyword Method for L2 Vocabulary Learning with Multimodal LLMscited by 0
- 2026Adaptive Prompt Elicitation for Text-to-Image Generationcited by 0
- 2026Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMscited by 0
- 2026Alignment Games: A Framework for Conceptual Repair in Human-AI Collaborationcited by 0
- 2026Alignment Games: A Framework for Conceptual Repair in Human-AI Collaboration28 Sep 2026
- 2026Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs5 Feb 2026
- 2026Adaptive Prompt Elicitation for Text-to-Image Generation4 Feb 2026
- 2026WordCraft: Scaffolding the Keyword Method for L2 Vocabulary Learning with Multimodal LLMs31 Jan 2026
- 2025AnimAgents: Coordinating Multi-Stage Animation Pre-Production with Human-Multi-Agent Collaboration22 Nov 2025
- 2025DesignerlyLoop: Forming Design Intent through Curated Reasoning for Human-LLM Alignment19 Nov 2025
- 2025StoryEnsemble: Enabling Dynamic Exploration & Iteration in the Design Process with AI and Forward-Backward Propagation5 Aug 2025
- 2025"I Know It When I See It": Mood Spaces for Connecting and Expressing Visual Concepts21 Apr 2025