洞察 - Web Development - # Automatic UI Code Generation from Design Images

VISION2UI: A Real-World Dataset for Generating UI Code from High-Fidelity Design Images

Q: How can the dataset be further expanded to include a wider range of web design styles and complexity levels?

To expand the dataset to encompass a broader range of web design styles and complexity levels, several strategies can be employed: Diverse Data Collection: Incorporate data from various sources beyond Common Crawl, such as design repositories, design competitions, and real-world websites. This will introduce a wider array of design styles and complexities. User-Generated Content: Encourage users to submit their design images for inclusion in the dataset. This can lead to a more diverse set of design styles reflecting individual creativity. Collaboration with Design Communities: Partner with design communities and forums to gather design images that showcase different styles and complexities. Engaging with professionals and enthusiasts can provide valuable insights. Augmented Data Generation: Use techniques like data augmentation to create variations of existing design images, altering colors, layouts, and elements to introduce diversity in the dataset. Incorporating Feedback Loops: Implement mechanisms for users to provide feedback on the dataset, suggesting new design styles or complexities to include. This iterative process can ensure continuous expansion and improvement.

Q: What architectural innovations or training techniques could help MLLMs better capture the hierarchical structure of HTML when generating code from design images?

To enhance MLLMs' ability to capture the hierarchical structure of HTML when generating code from design images, the following architectural innovations and training techniques can be considered: Multi-Modal Architectures: Incorporate both image and text modalities in the model architecture to enable better alignment between the design images and the generated HTML code. Attention Mechanisms: Implement attention mechanisms that focus on specific regions of the design images when generating corresponding HTML elements. This can help the model understand the spatial relationships and hierarchy. Structured Prediction: Train the model to predict the hierarchical structure of HTML elements directly, treating it as a structured prediction task. This can guide the model to generate code with the correct nesting and organization. Fine-Tuning on Layout Information: Utilize the layout information provided in the dataset to fine-tune the MLLMs specifically for capturing the hierarchical structure. This targeted training can improve the model's understanding of HTML layout. Ensemble Models: Combine multiple MLLMs specialized in different aspects of HTML generation, such as element positioning, nesting, and styling. Ensemble models can leverage the strengths of individual models to improve overall performance.

Q: How could the dataset be leveraged to develop tools that empower non-technical users to create web pages directly from design mockups?

The dataset can serve as a foundation for developing tools that empower non-technical users to create web pages from design mockups through the following approaches: Interactive Prototyping Tools: Build interactive prototyping tools that allow users to upload design images and receive generated HTML code in real-time. Users can iterate on the design and code simultaneously. Template Generation: Develop a template generation tool that suggests HTML code based on uploaded design images. Users can customize the templates to fit their needs, enabling quick webpage creation. Visual Drag-and-Drop Editors: Integrate the dataset into visual drag-and-drop editors that translate design elements into HTML components. Users can visually arrange elements and instantly generate corresponding code. Guided Code Generation: Implement a guided code generation tool that provides step-by-step instructions for converting design images into HTML code. Users can follow the prompts to create web pages efficiently. Feedback Mechanisms: Incorporate feedback mechanisms that allow users to provide input on the generated code, enabling the tool to learn and improve over time. This iterative process enhances user experience and code accuracy.

核心概念

The VISION2UI dataset provides a large-scale, real-world dataset with layout information to enable Multimodal Large Language Models (MLLMs) to effectively generate HTML code from high-fidelity design images.

摘要

The VISION2UI dataset was constructed by the authors to address the limitations of existing datasets for training MLLMs on the task of generating UI code from design images. The key highlights of the dataset are:

Data Collection: The dataset was extracted from the Common Crawl dataset, which contains a vast collection of real-world web pages. The authors downloaded the corresponding CSS and image elements, and then cleaned the HTML code by removing redundant elements and applying length filters.
Screenshots Generation: The authors used Pyppeteer to generate screenshots of the cleaned web pages and simultaneously captured the layout information (size and position) of each HTML element.
Filtering with Neural Scorer: To further improve the quality of the dataset, the authors trained a neural network model as a scorer to assess the aesthetics and completeness of the screenshots. Data points with low scores were filtered out.
Dataset Statistics: The final VISION2UI dataset consists of 20,000 (with much more coming soon) parallel samples of HTML code and UI design images, along with the layout information. Compared to existing datasets, VISION2UI exhibits significantly greater diversity in terms of HTML structure, element types, layout, and coloration.

The authors discuss the practical challenges of automatic HTML code generation from design images, such as the complexity of CSS and the difficulty in accurately capturing the HTML DOM tree structure. They plan to further enhance the dataset and explore effective model architectures to address these challenges.

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VISION2UI

by Yi Gui,Zhen ... 在 arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.06369.pdf

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How can the dataset be further expanded to include a wider range of web design styles and complexity levels?

To expand the dataset to encompass a broader range of web design styles and complexity levels, several strategies can be employed:

Diverse Data Collection: Incorporate data from various sources beyond Common Crawl, such as design repositories, design competitions, and real-world websites. This will introduce a wider array of design styles and complexities.
User-Generated Content: Encourage users to submit their design images for inclusion in the dataset. This can lead to a more diverse set of design styles reflecting individual creativity.
Collaboration with Design Communities: Partner with design communities and forums to gather design images that showcase different styles and complexities. Engaging with professionals and enthusiasts can provide valuable insights.
Augmented Data Generation: Use techniques like data augmentation to create variations of existing design images, altering colors, layouts, and elements to introduce diversity in the dataset.
Incorporating Feedback Loops: Implement mechanisms for users to provide feedback on the dataset, suggesting new design styles or complexities to include. This iterative process can ensure continuous expansion and improvement.

What architectural innovations or training techniques could help MLLMs better capture the hierarchical structure of HTML when generating code from design images?

To enhance MLLMs' ability to capture the hierarchical structure of HTML when generating code from design images, the following architectural innovations and training techniques can be considered:

Multi-Modal Architectures: Incorporate both image and text modalities in the model architecture to enable better alignment between the design images and the generated HTML code.
Attention Mechanisms: Implement attention mechanisms that focus on specific regions of the design images when generating corresponding HTML elements. This can help the model understand the spatial relationships and hierarchy.
Structured Prediction: Train the model to predict the hierarchical structure of HTML elements directly, treating it as a structured prediction task. This can guide the model to generate code with the correct nesting and organization.
Fine-Tuning on Layout Information: Utilize the layout information provided in the dataset to fine-tune the MLLMs specifically for capturing the hierarchical structure. This targeted training can improve the model's understanding of HTML layout.
Ensemble Models: Combine multiple MLLMs specialized in different aspects of HTML generation, such as element positioning, nesting, and styling. Ensemble models can leverage the strengths of individual models to improve overall performance.

How could the dataset be leveraged to develop tools that empower non-technical users to create web pages directly from design mockups?

The dataset can serve as a foundation for developing tools that empower non-technical users to create web pages from design mockups through the following approaches:

Interactive Prototyping Tools: Build interactive prototyping tools that allow users to upload design images and receive generated HTML code in real-time. Users can iterate on the design and code simultaneously.
Template Generation: Develop a template generation tool that suggests HTML code based on uploaded design images. Users can customize the templates to fit their needs, enabling quick webpage creation.
Visual Drag-and-Drop Editors: Integrate the dataset into visual drag-and-drop editors that translate design elements into HTML components. Users can visually arrange elements and instantly generate corresponding code.
Guided Code Generation: Implement a guided code generation tool that provides step-by-step instructions for converting design images into HTML code. Users can follow the prompts to create web pages efficiently.
Feedback Mechanisms: Incorporate feedback mechanisms that allow users to provide input on the generated code, enabling the tool to learn and improve over time. This iterative process enhances user experience and code accuracy.