29 citations · 29 across the 6 of their papers we have counts for
6 papers
Devil is in the Detail: Towards Injecting Fine Details of Image Prompt in Image Generation via Conflict-free Guidance and Stratified Attention
Kyungmin Jo, Jooyeol Yun, Jaegul Choo
While large-scale text-to-image diffusion models enable the generation of high-quality, diverse images from text prompts, these prompts struggle to capture intricate details, such…
Skip-and-Play: Depth-Driven Pose-Preserved Image Generation for Any Objects
Kyungmin Jo, Jaegul Choo
The emergence of diffusion models has enabled the generation of diverse high-quality images solely from text, prompting subsequent efforts to enhance the controllability of these m…
SideGAN: 3D-Aware Generative Model for Improved Side-View Image Synthesis
Kyungmin Jo, Wonjoon Jin, Jaegul Choo +2
While recent 3D-aware generative models have shown photo-realistic image synthesis with multi-view consistency, the synthesized image quality degrades depending on the camera pose…
Perspective Projection-Based 3D CT Reconstruction from Biplanar X-rays
Daeun Kyung, Kyungmin Jo, Jaegul Choo +2
X-ray computed tomography (CT) is one of the most common imaging techniques used to diagnose various diseases in the medical field. Its high contrast sensitivity and spatial resolu…
CG-NeRF: Conditional Generative Neural Radiance Fields
Kyungmin Jo, Gyumin Shim, Sanghun Jung +2
While recent NeRF-based generative models achieve the generation of diverse 3D-aware images, these approaches have limitations when generating images that contain user-specified ch…
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment
Jeonghoon Park, Kyungmin Jo, Daehoon Gwak +3
We evaluate the out-of-distribution (OOD) detection performance of self-supervised learning (SSL) techniques with a new evaluation framework. Unlike the previous evaluation methods…