  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.
