首个陶瓷釉料属性预测与图像生成数据集，助力艺术家高效设计釉料。
GlazyBench: A Benchmark for Ceramic Glaze Property Prediction and Image Generation
- 构建23,148个真实釉料配方数据集，支持属性预测与图像生成双任务。
- 基于传统机器学习和大模型的基线实验显示结果有潜力但仍有挑战。
- 为材料设计中的AI应用提供标准化评估基准，适合艺术与材料研究者。
陶瓷釉料开发因化学复杂性，长期依赖耗时费力的试错过程，给独立艺术家带来沉重负担。尽管多模态AI提供了现代化解决方案，但该领域缺乏大规模训练数据。本文提出GlazyBench，首个面向釉料设计的AI辅助数据集，包含23,148个真实釉料配方，支持两大任务：从原始材料预测烧制后表面属性（如颜色、透明度），以及根据属性生成准确的视觉图像。我们建立了基于传统机器学习与大语言模型的属性预测基线，以及基于深度生成模型与大型多模态模型的图像生成基准。实验表明结果具有前景但仍面临挑战。GlazyBench开创了AI辅助材料设计的新方向，为系统评估提供标准化基准。
原文摘要 · Abstract (English)
Developing ceramic glazes is a costly, time-consuming process of trial and error due to complex chemistry, placing a significant burden on independent artists. While recent advances in multimodal AI offer a modern solution, the field lacks the large-scale datasets required to train these models. We propose GlazyBench, the first dataset for AI-assisted glaze design. Comprising 23,148 real glaze formulations, GlazyBench supports two primary tasks: predicting post-firing surface properties, such as color and transparency, from raw materials, and generating accurate visual representations of the glaze based on these properties. We establish comprehensive baselines for property prediction using traditional machine learning and large language models, alongside image generation benchmarks using deep generative and large multimodal models. Our experiments demonstrate promising yet challenging results. GlazyBench pioneers a new research direction in AI-assisted material design, providing a standardized benchmark for systematic evaluation.
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