Multi-attribute Pizza Generator: Cross-domain Attribute Control with Conditional StyleGAN

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.

MPG: A Multi-ingredient Pizza Image Generator with Conditional StyleGANs

Multilabel conditional image generation is a challenging problem in computer vision. In this work we propose Multi-ingredient Pizza Generator (MPG), a conditional Generative Neural Network (GAN) framework for synthesizing multilabel images. We design MPG based on a state-of-the-art GAN structure called StyleGAN2, in which we develop a new conditioning technique by enforcing intermediate feature maps to learn scalewise label information. Because of the complex nature of the multilabel image generation problem, we also regularize synthetic image by predicting the corresponding ingredients as well as encourage the discriminator to distinguish between matched image and mismatched image. To verify the efficacy of MPG, we test it on Pizza10, which is a carefully annotated multi-ingredient pizza image dataset. MPG can successfully generate photo-realist pizza images with desired ingredients. The framework can be easily extend to other multilabel image generation scenarios.