由于复杂的化学成分, 给独立艺术家带来了沉重的负担，因此开发陶瓷釉料是一个成本高昂的, 耗时的反复试验过程。虽然多模式人工智能的最新进展提供了现代解决方案,，但该领域缺乏训练这些模型所需的大规模数据集。我们提出 GlazyBench, 作为人工智能辅助釉面设计的第一个数据集。包含 23,148 真实釉料配方, GlazyBench 支持两项主要任务: 预测烧成后表面特性,，例如原材料, 的颜色和透明度,，并根据这些特性生成釉料的准确视觉表示。我们使用传统机器学习和大型语言模型, 建立全面的属性预测基线，以及使用深度生成和大型多模态模型的图像生成基准。我们的实验证明了有希望但具有挑战性的结果。 GlazyBench 开创了人工智能辅助材料设计,的新研究方向，为系统评估提供了标准化基准。
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.
科目: 人工智能 (cs.AI); 计算机视觉和模式识别 (cs.CV)
Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)