https://arxiv.org/pdf/2504.18729

Overview

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.

Preparation

Download our repository

git clone https://github.com/HySonLab/Design2Code.git
cd Design2Code

Prepare conda environment and install dependencies (this step may take time)

conda env create -f environment.yml   # create graphui2code env
conda activate graphui2code           # activate

Install SAM model for segmentation and simfang.ttf font for visualization

wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth            # Install Segment Anything model

Training

Before running training script, please go to scripts/run_train.sh and modify your own LD_LIBRARY_PATH environment variable as follow:

export LD_LIBRARY_PATH="/path/to/miniconda3/envs/graphui2code/lib"

Then, run the script as follow:

sh scripts/run_train.sh

Inferencing

Before running inference script, also go to scripts/run_inference.sh and modify LD_LIBRARY_PATH as follow:

export LD_LIBRARY_PATH="/path/to/miniconda3/envs/graphui2code/lib"

Then, run the inference script:

sh scripts/run_inference.sh

Evaluation on Design2Code metrics

Design2Code metrics include Block-Match, Text, Position, Color, CLIP

Run the evaluation script as follow:

python eval/eval_design2code_metrics.py

Evaluation on traditional metrics

We also provide code to evaluation on traditional metrics including BLEU, MSE, SSIM, TreeBLEU, WeightedBLEU

python eval/eval_traditional_metrics.py

Reference

1. OpenFlamingo
2. Design2Code

Contact us

If you have any questions, comments or suggestions, please do not hesitate to contact us.

• Emails: vdtung21.work@gmail.com, thy@uab.edu

If you find our work useful, please cite it!

@misc{vu2025multimodalgraphrepresentationlearning,
      title={Multimodal graph representation learning for website generation based on visual sketch}, 
      author={Tung D. Vu and Chung Hoang and Truong-Son Hy},
      year={2025},
      eprint={2504.18729},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2504.18729}, 
}