Interaction2Code: How Far Are We From Automatic Interactive Webpage Generation?

• URL: http://arxiv.org/abs/2411.03292v1
• Date: Tue, 05 Nov 2024 17:40:03 GMT
• Title: Interaction2Code: How Far Are We From Automatic Interactive Webpage Generation?
• Authors: Jingyu Xiao, Yuxuan Wan, Yintong Huo, Zhiyao Xu, Michael R. Lyu,
• Abstract summary: We present the first systematic investigation of multi-modal large language models (MLLMs) in generating interactive webpages. Specifically, we first formulate the Interaction-to-Code task and build the Interaction2Code benchmark. We then conduct comprehensive experiments on three state-of-the-art (SOTA) MLLMs using both automatic metrics and human evaluations.
• Score: 30.540795619470483
• License:
• Abstract: Converting webpage design into functional UI code is a critical step for building websites, which can be labor-intensive and time-consuming. To automate this design-to-code transformation process, various automated methods using learning-based networks and multi-modal large language models (MLLMs) have been proposed. However, these studies were merely evaluated on a narrow range of static web pages and ignored dynamic interaction elements, making them less practical for real-world website deployment. To fill in the blank, we present the first systematic investigation of MLLMs in generating interactive webpages. Specifically, we first formulate the Interaction-to-Code task and build the Interaction2Code benchmark that contains 97 unique web pages and 213 distinct interactions, spanning 15 webpage types and 30 interaction categories. We then conduct comprehensive experiments on three state-of-the-art (SOTA) MLLMs using both automatic metrics and human evaluations, thereby summarizing six findings accordingly. Our experimental results highlight the limitations of MLLMs in generating fine-grained interactive features and managing interactions with complex transformations and subtle visual modifications. We further analyze failure cases and their underlying causes, identifying 10 common failure types and assessing their severity. Additionally, our findings reveal three critical influencing factors, i.e., prompts, visual saliency, and textual descriptions, that can enhance the interaction generation performance of MLLMs. Based on these findings, we elicit implications for researchers and developers, providing a foundation for future advancements in this field. Datasets and source code are available at https://github.com/WebPAI/Interaction2Code.

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