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arXiv · 2506.06733

RecipeGen: A Step-Aligned Multimodal Benchmark for Real-World Recipe Generation

Ruoxuan Zhang·Jidong Gao·Bin Wen·Hongxia Xie·Chenming Zhang·Hong-Han Shuai·Wen-Huang Cheng
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Abstract

Creating recipe images is a key challenge in food computing, with applications in culinary education and multimodal recipe assistants. However, existing datasets lack fine-grained alignment between recipe goals, step-wise instructions, and visual content. We present RecipeGen, the first large-scale, real-world benchmark for recipe-based Text-to-Image (T2I), Image-to-Video (I2V), and Text-to-Video (T2V) generation. RecipeGen contains 26,453 recipes, 196,724 images, and 4,491 videos, covering diverse ingredients, cooking procedures, styles, and dish types. We further propose domain-specific evaluation metrics to assess ingredient fidelity and interaction modeling, benchmark representative T2I, I2V, and T2V models, and provide insights for future recipe generation models. Project page is available now.

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Ruoxuan Zhang, Jidong Gao, Bin Wen, Hongxia Xie, Chenming Zhang, Hong-Han Shuai, Wen-Huang Cheng. 2025-06-07. RecipeGen: A Step-Aligned Multimodal Benchmark for Real-World Recipe Generation. https://arxiv.org/abs/2506.06733

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