Multimodal graph representation learning for website generation based on visual sketch
This addresses the challenge of automating website generation from visual sketches for software developers, offering a novel approach that enhances automation and efficiency in design-to-code conversion.
The paper tackles the Design2Code problem of converting digital designs into functional source code by proposing a multimodal graph representation learning method that integrates visual and structural information from sketches, resulting in significant improvements in accuracy and efficiency for generating semantically correct HTML code.
The Design2Code problem, which involves converting digital designs into functional source code, is a significant challenge in software development due to its complexity and time-consuming nature. Traditional approaches often struggle with accurately interpreting the intricate visual details and structural relationships inherent in webpage designs, leading to limitations in automation and efficiency. In this paper, we propose a novel method that leverages multimodal graph representation learning to address these challenges. By integrating both visual and structural information from design sketches, our approach enhances the accuracy and efficiency of code generation, particularly in producing semantically correct and structurally sound HTML code. We present a comprehensive evaluation of our method, demonstrating significant improvements in both accuracy and efficiency compared to existing techniques. Extensive evaluation demonstrates significant improvements of multimodal graph learning over existing techniques, highlighting the potential of our method to revolutionize design-to-code automation. Code available at https://github.com/HySonLab/Design2Code