Reverse Browser: Vector-Image-to-Code Generator
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Abstract (arXiv)
Automating the conversion of user interface design into code (image-to-code or image-to-UI) is an active area of software engineering research. However, the state-of-the-art solutions do not achieve high fidelity to the original design, as evidenced by benchmarks. In this work, I approach the problem differently: I use vector images instead of bitmaps as model input. I create several large datasets for training machine learning models. I evaluate the available array of Image Quality Assessment (IQA) algorithms and introduce a new, multi-scale metric. I then train a large open-weights model and discuss its limitations.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 51 references that are in the map. First posted on arXiv 5 Sep 2025.
Builds on
29 of 51 references.
References · 29
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17 more
- 2021LongT5: Efficient Text-To-Text Transformer for Long Sequencescited by 232
- 2023TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessmentcited by 226
- 2016Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessmentcited by 225
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- 2025Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domainscited by 156
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- 2024Transformers Can Do Arithmetic with the Right Embeddingscited by 78
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- 2024WebCode2M: A Real-World Dataset for Code Generation from Webpage Designscited by 44
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- 2024Pre-trained Large Language Models Use Fourier Features to Compute Additioncited by 39
- 2025UICopilot: Automating UI Synthesis via Hierarchical Code Generation from Webpage Designscited by 22
- 2019Sketch2code: Generating a website from a paper mockupcited by 20
- 2024Image2Struct: Benchmarking Structure Extraction for Vision-Language Modelscited by 19
- 2024Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprisecited by 17
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