Browse > Computer Vision > Image Generation

# Image Generation Edit

198 papers with code · Computer Vision

Image generation (synthesis) is the task of generating new images from an existing dataset.

• Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
• Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.

In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.

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# Fully Automated Image De-fencing using Conditional Generative Adversarial Networks

19 Aug 2019

Image de-fencing is one of the important aspects of recreational photography in which the objective is to remove the fence texture present in an image and generate an aesthetically pleasing version of the same image without the fence texture.

# Dual Adversarial Inference for Text-to-Image Synthesis

14 Aug 2019

), and the style, which is usually not well described in the text (e. g., location, quantity, size, etc.).

# Enforcing Perceptual Consistency on Generative Adversarial Networks by Using the Normalised Laplacian Pyramid Distance

9 Aug 2019

While an important part of the evaluation of the generated images usually involves visual inspection, the inclusion of human perception as a factor in the training process is often overlooked.

# Editing Text in the Wild

8 Aug 2019

Specifically, we propose an end-to-end trainable style retention network (SRNet) that consists of three modules: text conversion module, background inpainting module and fusion module.

# Relighting Humans: Occlusion-Aware Inverse Rendering for Full-Body Human Images

7 Aug 2019

Based on supervised learning using convolutional neural networks (CNNs), we infer not only an albedo map, illumination but also a light transport map that encodes occlusion as nine SH coefficients per pixel.

# SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis

6 Aug 2019

Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks.

# Visual-Relation Conscious Image Generation from Structured-Text

5 Aug 2019

The visual-relation layout module predicts bounding-boxes for all the entities given in an input text so that each of them uniquely corresponds to each entity while keeping its involved relationships.

# GAN Path Finder: Preliminary results

5 Aug 2019

2D path planning in static environment is a well-known problem and one of the common ways to solve it is to 1) represent the environment as a grid and 2) perform a heuristic search for a path on it.

# Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation

2 Aug 2019

In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (C$^2$GAN) for the task of keypoint-guided image generation.

# InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations

1 Aug 2019

We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ.