4 papers
Triadic Dynamics Aware Diffusion Posterior Sampling for Inverse Problems: Optimizing Guidance and Stochasticity Schedules
Junseo Bang, Dong Ju Mun, Hoigi Seo +2
Generative posterior sampling using diffusion models has emerged as a dominant paradigm for solving inverse problems in imaging, which usually consists of three main components: da…
DiffBMP: Differentiable Rendering with Bitmap Primitives
Seongmin Hong, Junghun James Kim, Daehyeop Kim +2
We introduce DiffBMP, a scalable and efficient differentiable rendering engine for a collection of bitmap images. Our work addresses a limitation that traditional differentiable re…
MOST: MR reconstruction Optimization for multiple downStream Tasks via continual learning
Hwihun Jeong, Se Young Chun, Jongho Lee
Deep learning-based Magnetic Resonance (MR) reconstruction methods have focused on generating high-quality images but often overlook the impact on downstream tasks (e.g., segmentat…
PersonaCraft: Personalized and Controllable Full-Body Multi-Human Scene Generation Using Occlusion-Aware 3D-Conditioned Diffusion
Gwanghyun Kim, Suh Yoon Jeon, Seunggyu Lee +1
We present PersonaCraft, a framework for controllable and occlusion-robust full-body personalized image synthesis of multiple individuals in complex scenes. Current methods struggl…