Generative Adversarial Networks in the built environment: A comprehensive review of the application of GANs across data types and scales
Abstract Generative Adversarial Networks (GANs) are a type of deep neural network that
have achieved many state-of-the-art results for generative tasks. GANs can be useful in the …
have achieved many state-of-the-art results for generative tasks. GANs can be useful in the …
A comprehensive review of past and present image inpainting methods
Images can be described as visual representations or likeness of something (person or
object) which can be reproduced or captured, eg a hand drawing, photographic material …
object) which can be reproduced or captured, eg a hand drawing, photographic material …
Repaint: Inpainting using denoising diffusion probabilistic models
Free-form inpainting is the task of adding new content to an image in the regions specified
by an arbitrary binary mask. Most existing approaches train for a certain distribution of …
by an arbitrary binary mask. Most existing approaches train for a certain distribution of …
Mat: Mask-aware transformer for large hole image inpainting
Recent studies have shown the importance of modeling long-range interactions in the
inpainting problem. To achieve this goal, existing approaches exploit either standalone …
inpainting problem. To achieve this goal, existing approaches exploit either standalone …
Palette: Image-to-image diffusion models
This paper develops a unified framework for image-to-image translation based on
conditional diffusion models and evaluates this framework on four challenging image-to …
conditional diffusion models and evaluates this framework on four challenging image-to …
Image inpainting via conditional texture and structure dual generation
Deep generative approaches have recently made considerable progress in image
inpainting by introducing structure priors. Due to the lack of proper interaction with image …
inpainting by introducing structure priors. Due to the lack of proper interaction with image …
Deep learning for image inpainting: A survey
Image inpainting has been widely exploited in the field of computer vision and image
processing. The main purpose of image inpainting is to produce visually plausible structure …
processing. The main purpose of image inpainting is to produce visually plausible structure …
Image-to-image translation: Methods and applications
Image-to-image translation (I2I) aims to transfer images from a source domain to a target
domain while preserving the content representations. I2I has drawn increasing attention and …
domain while preserving the content representations. I2I has drawn increasing attention and …
Pd-gan: Probabilistic diverse gan for image inpainting
We propose PD-GAN, a probabilistic diverse GAN forimage inpainting. Given an input image
with arbitrary holeregions, PD-GAN produces multiple inpainting results withdiverse and …
with arbitrary holeregions, PD-GAN produces multiple inpainting results withdiverse and …
High-fidelity pluralistic image completion with transformers
Image completion has made tremendous progress with convolutional neural networks
(CNNs), because of their powerful texture modeling capacity. However, due to some …
(CNNs), because of their powerful texture modeling capacity. However, due to some …