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

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arxiv 2502.21219 v1 pith:6G5DJFG7 submitted 2025-02-28 cs.HC

classification cs.HC
keywords designvisualbrickifyintentlexiconthemtokensdesigners
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SceneLoom: Communicating Data with Scene Context

    cs.HC 2025-07 conditional novelty 6.0 of 10

    SceneLoom guides a vision-language model through a design space derived from 54 data videos to generate chart-in-image designs aligned with user narrative intent.

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