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.
@article{arxiv.2506.06733,
title = {RecipeGen: A Step-Aligned Multimodal Benchmark for Real-World Recipe Generation},
author = {Ruoxuan Zhang and Jidong Gao and Bin Wen and Hongxia Xie and Chenming Zhang and Hong-Han Shuai and Wen-Huang Cheng},
journal= {arXiv preprint arXiv:2506.06733},
year = {2025}
}