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Found 16 papers, 11 papers with code
Visual Style Prompting with Swapping Self-Attention
Despite their remarkable capability, existing models still face challenges in achieving controlled generation with a consistent style, requiring costly fine-tuning or often inadequately transferring the visual elements due to content leakage.
User-friendly Image Editing with Minimal Text Input: Leveraging Captioning and Injection Techniques
From the users' standpoint, prompt engineering is a labor-intensive process, and users prefer to provide a target word for editing instead of a full sentence.
Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models
Text-to-image diffusion models can generate diverse, high-fidelity images based on user-provided text prompts.
Learning Input-agnostic Manipulation Directions in StyleGAN with Text Guidance
With the advantages of fast inference and human-friendly flexible manipulation, image-agnostic style manipulation via text guidance enables new applications that were not previously available.
Diffusion Video Autoencoders: Toward Temporally Consistent Face Video Editing via Disentangled Video Encoding
Inspired by the impressive performance of recent face image editing methods, several studies have been naturally proposed to extend these methods to the face video editing task.
Generator Knows What Discriminator Should Learn in Unconditional GANs
Here we explore the efficacy of dense supervision in unconditional generation and find generator feature maps can be an alternative of cost-expensive semantic label maps.
Memory Efficient Patch-based Training for INR-based GANs
However, training existing approaches require a heavy computational cost proportional to the image resolution, since they compute an MLP operation for every (x, y) coordinate.
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images
In this work, we propose a new evaluation metric, called `rarity score', to measure the individual rarity of each image synthesized by generative models.
Feature Statistics Mixing Regularization for Generative Adversarial Networks
In generative adversarial networks, improving discriminators is one of the key components for generation performance.
Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing
Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from i) time-consuming optimization for projecting real images to the latent vectors, ii) or inaccurate embedding through an encoder.
A StyleMap-Based Generator for Real-Time Image Projection and Local Editing
State-of-the-art GAN-based methods for editing real images suffer from time-consuming operations in projecting real images to latent vectors.
Rethinking the Truly Unsupervised Image-to-Image Translation
To this end, we propose a truly unsupervised image-to-image translation model (TUNIT) that simultaneously learns to separate image domains and translates input images into the estimated domains.
Reliable Fidelity and Diversity Metrics for Generative Models
In this paper, we show that even the latest version of the precision and recall metrics are not reliable yet.
StarGAN v2: Diverse Image Synthesis for Multiple Domains
A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains.
Fundus to Angiography Generation
Multimodal Unsupervised Image-To-Image Translation
+1
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model.
Ranked #1 on
Image-to-Image Translation
on RaFD
(using extra training data)
