LaTCoder: Converting Webpage Design to Code with Layout-as-Thought
Authors: name: Yi Gui, name: Zhen Li, name: Zhongyi Zhang, name: Guohao Wang, name: Tianpeng Lv, name: Gaoyang Jiang, name: Yi Liu, name: Dongping Chen, name: Yao Wan, name: Hongyu Zhang, name: Wenbin Jiang, name: Xuanhua Shi, name: Hai Jin

Converting webpage designs into code (design-to-code) plays a vital role in
User Interface (UI) development for front-end developers, bridging the gap
between visual design and functional implementation. While recent Multimodal
Large Language Models (MLLMs) have shown significant potential in
design-to-code tasks, they often fail to accurately preserve the layout during
code generation. To this end, we draw inspiration from the Chain-of-Thought
(CoT) reasoning in human cognition and propose LaTCoder, a novel approach that
enhances layout preservation in webpage design during code generation with
Layout-as-Thought (LaT). Specifically, we first introduce a simple yet
efficient algorithm to divide the webpage design into image blocks. Next, we
prompt MLLMs using a CoTbased approach to generate code for each block.
Finally, we apply two assembly strategies-absolute positioning and an
MLLM-based method-followed by dynamic selection to determine the optimal
output. We evaluate the effectiveness of LaTCoder using multiple backbone MLLMs
(i.e., DeepSeek-VL2, Gemini, and GPT-4o) on both a public benchmark and a newly
introduced, more challenging benchmark (CC-HARD) that features complex layouts.
The experimental results on automatic metrics demonstrate significant
improvements. Specifically, TreeBLEU scores increased by 66.67% and MAE
decreased by 38% when using DeepSeek-VL2, compared to direct prompting.
Moreover, the human preference evaluation results indicate that annotators
favor the webpages generated by LaTCoder in over 60% of cases, providing strong
evidence of the effectiveness of our method.