CookGAN: Meal Image Synthesis from Ingredients

TL;DR

CookGAN is a novel deep learning framework that synthesizes realistic meal images from ingredient lists, effectively capturing complex food appearances and cooking effects, advancing food image generation technology.

Contribution

The paper introduces CookGAN, a new generative model with attention and cycle consistency for realistic meal image synthesis from ingredients, addressing the complexity of food images.

Findings

CookGAN successfully generates meal images from ingredients.

The model captures cooking effects and ingredient details.

Results outperform baseline methods in realism and accuracy.

Abstract

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.