  In this work we propose a new computational framework, based on generative
deep models, for synthesis of photo-realistic food meal images from textual
descriptions of its ingredients. Previous works on synthesis of images from
text typically rely on pre-trained text models to extract text features,
followed by a generative neural networks (GANs) 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. In contrast, meal images are significantly more complex, consisting of
multiple ingredients whose appearance and spatial qualities are further
modified by cooking methods. We propose a method that 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. Extensive experiments show our model is able to
generate meal image corresponding to the ingredients, which could be used to
augment existing dataset for solving other computational food analysis
problems.
