  Multi-attribute conditional image generation is a challenging problem in
computervision. We propose Multi-attribute Pizza Generator (MPG), a conditional
Generative Neural Network (GAN) framework for synthesizing images from a
trichotomy of attributes: content, view-geometry, and implicit visual style. We
design MPG by extending the state-of-the-art StyleGAN2, using a new
conditioning technique that guides the intermediate feature maps to learn
multi-scale multi-attribute entangled representationsof controlling attributes.
Because of the complex nature of the multi-attribute image generation problem,
we regularize the image generation by predicting the explicit conditioning
attributes (ingredients and view). To synthesize a pizza image with view
attributesoutside the range of natural training images, we design a CGI pizza
dataset PizzaView using 3D pizza models and employ it to train a view attribute
regressor to regularize the generation process, bridging the real and CGI
training datasets. To verify the efficacy of MPG, we test it on Pizza10, a
carefully annotated multi-ingredient pizza image dataset. MPG can successfully
generate photo-realistic pizza images with desired ingredients and view
attributes, beyond the range of those observed in real-world training data.
