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

arXiv: https://arxiv.org/abs/1612.00215 · HTML: https://mlsanity.com/paper/1612.00215/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2016.

## Built on by (most cited first, 21 of 21)

- [LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation](https://mlsanity.com/paper/2303.17189/) (2023)
- [Improving Diffusion Models for Scene Text Editing with Dual Encoders](https://mlsanity.com/paper/2304.05568/) (2023)
- [DiffFacto: Controllable Part-Based 3D Point Cloud Generation with Cross Diffusion](https://mlsanity.com/paper/2305.01921/) (2023)
- [View Generalization for Single Image Textured 3D Models](https://mlsanity.com/paper/2106.06533/) (2021)
- [PanoDR: Spherical Panorama Diminished Reality for Indoor Scenes](https://mlsanity.com/paper/2106.00446/) (2021)
- [A Diffusion Model Translator for Efficient Image-to-Image Translation](https://mlsanity.com/paper/2502.00307/) (2025)
- [AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Style](https://mlsanity.com/paper/2103.13722/) (2021)
- [DC-ControlNet: Decoupling Inter- and Intra-Element Conditions in Image Generation with Diffusion Models](https://mlsanity.com/paper/2502.14779/) (2025)
- [Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2203.12707/) (2022)
- [FASTER: A Font-Agnostic Scene Text Editing and Rendering Framework](https://mlsanity.com/paper/2308.02905/) (2023)
- [Domain Adaptation of Synthetic Driving Datasets for Real-World Autonomous Driving](https://mlsanity.com/paper/2302.04149/) (2023)
- [DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis](https://mlsanity.com/paper/2107.13087/) (2021)
- [Harnessing the Conditioning Sensorium for Improved Image Translation](https://mlsanity.com/paper/2110.06443/) (2021)
- [Rethinking Machine Learning Robustness via its Link with the Out-of-Distribution Problem](https://mlsanity.com/paper/2202.08944/) (2022)
- [How to Guide Adaptive Depth Sampling?](https://mlsanity.com/paper/2205.10202/) (2022)
- [An Architecture for the detection of GAN-generated Flood Images with Localization Capabilities](https://mlsanity.com/paper/2205.07073/) (2022)
- [AptSim2Real: Approximately-Paired Sim-to-Real Image Translation](https://mlsanity.com/paper/2303.12704/) (2023)
- [Why Adversarial Interaction Creates Non-Homogeneous Patterns: A Pseudo-Reaction-Diffusion Model for Turing Instability](https://mlsanity.com/paper/2010.00521/) (2020)
- [Adaptive Appearance Rendering](https://mlsanity.com/paper/2104.11931/) (2021)
- [Style-Restricted GAN: Multi-Modal Translation with Style Restriction Using Generative Adversarial Networks](https://mlsanity.com/paper/2105.07621/) (2021)
- [Cascading Modular Network (CAM-Net) for Multimodal Image Synthesis](https://mlsanity.com/paper/2106.09015/) (2021)

## Abstract (arXiv)

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.