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 …
Vqgan-clip: Open domain image generation and editing with natural language guidance
K Crowson, S Biderman, D Kornis, D Stander… - … on Computer Vision, 2022 - Springer
Generating and editing images from open domain text prompts is a challenging task that
heretofore has required expensive and specially trained models. We demonstrate a novel …
heretofore has required expensive and specially trained models. We demonstrate a novel …
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 …
Deep learning technique for human parsing: A survey and outlook
Human parsing aims to partition humans in image or video into multiple pixel-level semantic
parts. In the last decade, it has gained significantly increased interest in the computer vision …
parts. In the last decade, it has gained significantly increased interest in the computer vision …
You only need adversarial supervision for semantic image synthesis
Despite their recent successes, GAN models for semantic image synthesis still suffer from
poor image quality when trained with only adversarial supervision. Historically, additionally …
poor image quality when trained with only adversarial supervision. Historically, additionally …
Enhancing photorealism enhancement
We present an approach to enhancing the realism of synthetic images. The images are
enhanced by a convolutional network that leverages intermediate representations produced …
enhanced by a convolutional network that leverages intermediate representations produced …
Image inpainting guided by coherence priors of semantics and textures
Existing inpainting methods have achieved promising performance in recovering defected
images of specific scenes. However, filling holes involving multiple semantic categories …
images of specific scenes. However, filling holes involving multiple semantic categories …
Image inpainting with cascaded modulation gan and object-aware training
Recent image inpainting methods have made great progress but often struggle to generate
plausible image structures when dealing with large holes in complex images. This is …
plausible image structures when dealing with large holes in complex images. This is …
OASIS: only adversarial supervision for semantic image synthesis
Despite their recent successes, generative adversarial networks (GANs) for semantic image
synthesis still suffer from poor image quality when trained with only adversarial supervision …
synthesis still suffer from poor image quality when trained with only adversarial supervision …
Latentpaint: Image inpainting in latent space with diffusion models
C Corneanu, R Gadde… - Proceedings of the IEEE …, 2024 - openaccess.thecvf.com
Image inpainting is generally done using either a domain-specific (preconditioned) model or
a generic model that is postconditioned at inference time. Preconditioned models are fast at …
a generic model that is postconditioned at inference time. Preconditioned models are fast at …