TaoD2C-Bench: Benchmarking MLLMs for Industrial UI Code Generation Beyond Visual Fidelity
To fill this gap, we present TaoD2C-Bench, a benchmark for evaluating MLLMs' ability to generate UI code that satisfies implementation requirements in industrial applications.
ProofPaper ↗
Key points
- A key challenge for multimodal large language models (MLLMs) is moving beyond visual recognition to constraint-aware cross-modal reasoning.
- This challenge is acutely evident in industrial design-to-code (D2C), which converts user interface (UI) designs into code and requires MLLMs to connect design images with disorganized layer metadata, infer component and layout implementation requirements, and realize them in code under target-library constraints.
- TaoD2C-Bench defines three tasks: end-to-end UI code generation, requirement inference, and requirement realization.
- We release TaoD2C to support research on industrial UI code generation.
Sources (1)
- [1]TaoD2C-Bench: Benchmarking MLLMs for Industrial UI Code Generation Beyond Visual FidelityarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 04:38 PM
To fill this gap, we present TaoD2C-Bench, a benchmark for evaluating MLLMs' ability to generate UI code that satisfies implementation requirements in industrial applications.
A key challenge for multimodal large language models (MLLMs) is moving beyond visual recognition to constraint-aware cross-modal reasoning.
Extractive summary: sentences quoted from the sources.