Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts

Abstract

Automatic image synthesis research has been rapidly growing with deep networks getting more and more expressive. In the last couple of years, we have observed images of digits, indoor scenes, birds, chairs, etc. being automatically generated. The expressive power of image generators have also been enhanced by introducing several forms of conditioning variables such as object names, sentences, bounding box and key-point locations. In this work, we propose a novel deep conditional generative adversarial network architecture that takes its strength from the semantic layout and scene attributes integrated as conditioning variables. We show that our architecture is able to generate realistic outdoor scene images under different conditions, e.g. day-night, sunny-foggy, with clear object boundaries.

References in corpus (5)

• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
• DRAW: A Recurrent Neural Network For Image Generation
• Generating Videos with Scene Dynamics
• Deep Convolutional Inverse Graphics Network

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• GANILLA: Generative Adversarial Networks for Image to Illustration Translation
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• Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
• Harnessing the Conditioning Sensorium for Improved Image Translation
• Cascading Modular Network (CAM-Net) for Multimodal Image Synthesis
• Instance Map based Image Synthesis with a Denoising Generative Adversarial Network