Design2Code: How Far Are We From Automating Front-End Engineering?
Authors: name: Chenglei Si, name: Yanzhe Zhang, name: Zhengyuan Yang, name: Ruibo Liu, name: Diyi Yang

Generative AI has made rapid advancements in recent years, achieving
unprecedented capabilities in multimodal understanding and code generation.
This can enable a new paradigm of front-end development, in which multimodal
LLMs might directly convert visual designs into code implementations. In this
work, we formalize this as a Design2Code task and conduct comprehensive
benchmarking. Specifically, we manually curate a benchmark of 484 diverse
real-world webpages as test cases and develop a set of automatic evaluation
metrics to assess how well current multimodal LLMs can generate the code
implementations that directly render into the given reference webpages, given
the screenshots as input. We also complement automatic metrics with
comprehensive human evaluations. We develop a suite of multimodal prompting
methods and show their effectiveness on GPT-4V and Gemini Pro Vision. We
further finetune an open-source Design2Code-18B model that successfully matches
the performance of Gemini Pro Vision. Both human evaluation and automatic
metrics show that GPT-4V performs the best on this task compared to other
models. Moreover, annotators think GPT-4V generated webpages can replace the
original reference webpages in 49% of cases in terms of visual appearance and
content; and perhaps surprisingly, in 64% of cases GPT-4V generated webpages
are considered better than the original reference webpages. Our fine-grained
break-down metrics indicate that open-source models mostly lag in recalling
visual elements from the input webpages and in generating correct layout
designs, while aspects like text content and coloring can be drastically
improved with proper finetuning.