SetMealAsYouLike
Yuma Honbu, Keiji Yanai · 2022
By using semantic segmentation dataset with pixel-wise annotation for training GANs, image generation from a given mask image drawn by a user is possible. However, regarding mask-based food image synthesis, the existing food segmentation datasets have only food region masks and no plate region masks. When we train a mask-based image synthesis network with the datasets without plate mask annotation, the plate regions in the generated food images are uncontrollable by a user and tend to be distorted. To solve this problem, we use a Few-shot segmentation method to estimate the plate regions of the image in the existing food segmentation dataset using a limited number of plate region annotations, and add dish region masks to it. By using added plate masks as training data, we enable generating food images under the control of the shape of the plates. We have implemented the interactive food image drawing system in which we draw food masks as well as plate masks. In the demo, we demonstrate that we generate natural set meal images which include multiple dishes by the sketch interface easily.