# MC-GAN: Multi-conditional Generative Adversarial Network for Image Synthesis

arXiv: https://arxiv.org/abs/1805.01123 · HTML: https://mlsanity.com/paper/1805.01123/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2018.

## Built on by (most cited first, 10 of 10)

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- [Story Visualization by Online Text Augmentation with Context Memory](https://mlsanity.com/paper/2308.07575/) (2023)
- [GANTL: Towards Practical and Real-Time Topology Optimization with Conditional GANs and Transfer Learning](https://mlsanity.com/paper/2105.03045/) (2021)
- [BATINet: Background-Aware Text to Image Synthesis and Manipulation Network](https://mlsanity.com/paper/2308.05921/) (2023)
- [Art Creation with Multi-Conditional StyleGANs](https://mlsanity.com/paper/2202.11777/) (2022)
- [MeronymNet: A Hierarchical Approach for Unified and Controllable Multi-Category Object Generation](https://mlsanity.com/paper/2110.08818/) (2021)
- [GAS-NeXt: Few-Shot Cross-Lingual Font Generator](https://mlsanity.com/paper/2212.02886/) (2022)

## Abstract (arXiv)

In this paper, we introduce a new method for generating an object image from text attributes on a desired location, when the base image is given. One step further to the existing studies on text-to-image generation mainly focusing on the object's appearance, the proposed method aims to generate an object image preserving the given background information, which is the first attempt in this field. To tackle the problem, we propose a multi-conditional GAN (MC-GAN) which controls both the object and background information jointly. As a core component of MC-GAN, we propose a synthesis block which disentangles the object and background information in the training stage. This block enables MC-GAN to generate a realistic object image with the desired background by controlling the amount of the background information from the given base image using the foreground information from the text attributes. From the experiments with Caltech-200 bird and Oxford-102 flower datasets, we show that our model is able to generate photo-realistic images with a resolution of 128 x 128. The source code of MC-GAN is released.