29 citations · 29 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…