TaoD2C-Bench

Benchmarking MLLMs for Industrial UI Code Generation Beyond Visual Fidelity

Chengwei Shi · Yunnong Chen · Tingting Zhou · Qiang Lu · Shiyu Yue · Xinyuan Hu · Jianfang Ru · Liuqing Chen

Zhejiang University · Alibaba Group · Taobao (China) Software Co., Ltd.

2,861Production designs
97,652Expert annotations
17Commercial platforms
4Requirement categories

Abstract

TaoD2C-Bench evaluates whether multimodal large language models can generate industrial UI code that satisfies implementation requirements—not merely reproduce visual appearance. TaoD2C pairs real production designs with layer-level expert annotations that distinguish required constraints from permitted implementation choices. The benchmark separates end-to-end generation into requirement inference and requirement realization, exposing capability gaps that visual similarity alone cannot reveal.

Benchmark Overview

Overview of the TaoD2C-Bench tasks and evaluation framework

Three Evaluation Tasks

TASK 01

End-to-End Generation

Generate executable UI code from the design image and structured layer representation.

TASK 02

Requirement Inference

Infer the implementation requirements encoded implicitly in visual and layer-level evidence.

TASK 03

Requirement Realization

Produce code that correctly implements explicitly provided requirements under target-library constraints.

Expert Annotation Dimensions

ComponentGroupingPositionAlignment