# CookGAN: Meal Image Synthesis from Ingredients

arXiv: https://arxiv.org/abs/2002.11493 · HTML: https://mlsanity.com/paper/2002.11493/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 14 of 14)

- [Large Scale Visual Food Recognition](https://mlsanity.com/paper/2103.16107/) (2021)
- [AnyFace: Free-style Text-to-Face Synthesis and Manipulation](https://mlsanity.com/paper/2203.15334/) (2022)
- [Learning Program Representations for Food Images and Cooking Recipes](https://mlsanity.com/paper/2203.16071/) (2022)
- [The Future of Consumer Edge-AI Computing](https://mlsanity.com/paper/2210.10514/) (2022)
- [ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image Generation](https://mlsanity.com/paper/2409.12010/) (2024)
- [Learning to Substitute Ingredients in Recipes](https://mlsanity.com/paper/2302.07960/) (2023)
- [Cross-Modal Coherence for Text-to-Image Retrieval](https://mlsanity.com/paper/2109.11047/) (2021)
- [Diffusion Model with Clustering-based Conditioning for Food Image Generation](https://mlsanity.com/paper/2309.00199/) (2023)
- [Conditional Synthetic Food Image Generation](https://mlsanity.com/paper/2303.09005/) (2023)
- [Transformer-based Cross-Modal Recipe Embeddings with Large Batch Training](https://mlsanity.com/paper/2205.04948/) (2022)
- [RecipeGen: A Benchmark for Real-World Recipe Image Generation](https://mlsanity.com/paper/2503.05228/) (2025)
- [CookAnything: A Framework for Flexible and Consistent Multi-Step Recipe Image Generation](https://mlsanity.com/paper/2512.03540/) (2025)
- [Multi-attribute Pizza Generator: Cross-domain Attribute Control with Conditional StyleGAN](https://mlsanity.com/paper/2110.11830/) (2021)
- [RecipeGen: A Step-Aligned Multimodal Benchmark for Real-World Recipe Generation](https://mlsanity.com/paper/2506.06733/) (2025)

## Abstract (arXiv)

In this work we propose a new computational framework, based on generative deep models, for synthesis of photo-realistic food meal images from textual list of its ingredients. Previous works on synthesis of images from text typically rely on pre-trained text models to extract text features, followed by generative neural networks (GAN) aimed to generate realistic images conditioned on the text features. These works mainly focus on generating spatially compact and well-defined categories of objects, such as birds or flowers, but meal images are significantly more complex, consisting of multiple ingredients whose appearance and spatial qualities are further modified by cooking methods. To generate real-like meal images from ingredients, we propose Cook Generative Adversarial Networks (CookGAN), CookGAN first builds an attention-based ingredients-image association model, which is then used to condition a generative neural network tasked with synthesizing meal images. Furthermore, a cycle-consistent constraint is added to further improve image quality and control appearance. Experiments show our model is able to generate meal images corresponding to the ingredients.