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

arXiv: https://arxiv.org/abs/2012.02821 · HTML: https://mlsanity.com/paper/2012.02821/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models](https://mlsanity.com/paper/2205.03859/) (2022)
- [Diffusion Model with Clustering-based Conditioning for Food Image Generation](https://mlsanity.com/paper/2309.00199/) (2023)
- [Conditional Synthetic Food Image Generation](https://mlsanity.com/paper/2303.09005/) (2023)
- [Shape-Preserving Generation of Food Images for Automatic Dietary Assessment](https://mlsanity.com/paper/2408.13358/) (2024)
- [The Work of Art in an Age of Mechanical Generation](https://mlsanity.com/paper/2101.11587/) (2021)

## Abstract (arXiv)

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.