96 citations · 174 across the 14 of their papers we have counts for
16 papers
StyleKeeper: Prevent Content Leakage using Negative Visual Query Guidance
Jaeseok Jeong, Junho Kim, Gayoung Lee +2
In the domain of text-to-image generation, diffusion models have emerged as powerful tools. Recently, studies on visual prompting, where images are used as prompts, have enabled mo…
Visual Style Prompting with Swapping Self-Attention
Jaeseok Jeong, Junho Kim, Yunjey Choi +2
In the evolving domain of text-to-image generation, diffusion models have emerged as powerful tools in content creation. Despite their remarkable capability, existing models still…
User-friendly Image Editing with Minimal Text Input: Leveraging Captioning and Injection Techniques
Sunwoo Kim, Wooseok Jang, Hyunsu Kim +4
Recent text-driven image editing in diffusion models has shown remarkable success. However, the existing methods assume that the user's description sufficiently grounds the context…
Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models
Jooyoung Choi, Yunjey Choi, Yunji Kim +2
Text-to-image diffusion models can generate diverse, high-fidelity images based on user-provided text prompts. Recent research has extended these models to support text-guided imag…
Learning Input-agnostic Manipulation Directions in StyleGAN with Text Guidance
Yoonjeon Kim, Hyunsu Kim, Junho Kim +2
With the advantages of fast inference and human-friendly flexible manipulation, image-agnostic style manipulation via text guidance enables new applications that were not previousl…
3D-aware Blending with Generative NeRFs
Hyunsu Kim, Gayoung Lee, Yunjey Choi +2
Image blending aims to combine multiple images seamlessly. It remains challenging for existing 2D-based methods, especially when input images are misaligned due to differences in 3…
Diffusion Video Autoencoders: Toward Temporally Consistent Face Video Editing via Disentangled Video Encoding
Gyeongman Kim, Hajin Shim, Hyunsu Kim +3
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. On…
Generator Knows What Discriminator Should Learn in Unconditional GANs
Gayoung Lee, Hyunsu Kim, Junho Kim +3
Recent methods for conditional image generation benefit from dense supervision such as segmentation label maps to achieve high-fidelity. However, it is rarely explored to employ de…
Memory Efficient Patch-based Training for INR-based GANs
Namwoo Lee, Hyunsu Kim, Gayoung Lee +2
Recent studies have shown remarkable progress in GANs based on implicit neural representation (INR) - an MLP that produces an RGB value given its (x, y) coordinate. They represent…
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images
Jiyeon Han, Hwanil Choi, Yunjey Choi +3
Evaluation metrics in image synthesis play a key role to measure performances of generative models. However, most metrics mainly focus on image fidelity. Existing diversity metrics…
Feature Statistics Mixing Regularization for Generative Adversarial Networks
Junho Kim, Yunjey Choi, Youngjung Uh
In generative adversarial networks, improving discriminators is one of the key components for generation performance. As image classifiers are biased toward texture and debiasing i…
Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing
Hyunsu Kim, Yunjey Choi, Junho Kim +2
Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. Although manipulating the latent vectors controls the synthesized outputs, editing re…
Rethinking the Truly Unsupervised Image-to-Image Translation
Kyungjune Baek, Yunjey Choi, Youngjung Uh +2
Every recent image-to-image translation model inherently requires either image-level (i.e. input-output pairs) or set-level (i.e. domain labels) supervision. However, even set-leve…
Reliable Fidelity and Diversity Metrics for Generative Models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh +2
Devising indicative evaluation metrics for the image generation task remains an open problem. The most widely used metric for measuring the similarity between real and generated im…
StarGAN v2: Diverse Image Synthesis for Multiple Domains
Yunjey Choi, Youngjung Uh, Jaejun Yoo +1
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) s…
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
Yunjey Choi, Minje Choi, Munyoung Kim +3
Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more th…