10 citations · 14 across the 16 of their papers we have counts for
16 papers
InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem
Yeobin Hong, Suhyeon Lee, Hyungjin Chung +1
Recent approaches in controllable novel view video generation often rely on fine-tuning pre-trained Video Diffusion Models (VDMs). This dominant paradigm is computationally expensi…
ReDirector: Creating Any-Length Video Retakes with Rotary Camera Encoding
Byeongjun Park, Byung-Hoon Kim, Hyungjin Chung +1
We present ReDirector, a novel camera-controlled video retake generation method for dynamically captured variable-length videos. In particular, we rectify a common misuse of RoPE i…
Training-Free Reward-Guided Image Editing via Trajectory Optimal Control
Jinho Chang, Jaemin Kim, Jong Chul Ye
Recent advancements in diffusion and flow-matching models have demonstrated remarkable capabilities in high-fidelity image synthesis. A prominent line of research involves reward-g…
Universal Reasoner: A Single, Composable Plug-and-Play Reasoner for Frozen LLMs
Jaemin Kim, Hangeol Chang, Hyunmin Hwang +2
Large Language Models (LLMs) have demonstrated remarkable general capabilities, but enhancing skills such as reasoning often demands substantial computational resources and may com…
InvFusion: Bridging Supervised and Zero-shot Diffusion for Inverse Problems
Noam Elata, Hyungjin Chung, Jong Chul Ye +2
Diffusion Models have demonstrated remarkable capabilities in handling inverse problems, offering high-quality posterior-sampling-based solutions. Despite significant advances, a f…
ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning
Hyungjin Chung, Dohun Lee, Zihui Wu +3
Compressed sensing MRI seeks to accelerate MRI acquisition processes by sampling fewer k-space measurements and then reconstructing the missing data algorithmically. The success of…
ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts
Jinho Chang, Changsun Lee, Hyungjin Chung +1
As Classifier-Free Guidance (CFG) has proven effective in conditional diffusion model sampling for improved condition alignment, many applications use a negated CFG term as a Negat…
FreeGuide: Training-Free Text-to-Video Alignment using Image LVLM
Jaemin Kim, Bryan Sangwoo Kim, Jong Chul Ye
Diffusion models have achieved impressive results in generative tasks for text-to-video (T2V) synthesis. However, achieving accurate text alignment in T2V generation remains challe…
Optical-Flow Guided Prompt Optimization for Coherent Video Generation
Hyelin Nam, Jaemin Kim, Dohun Lee +1
While text-to-video diffusion models have made significant strides, many still face challenges in generating videos with temporal consistency. Within diffusion frameworks, guidance…
Derivative-Free Diffusion Manifold-Constrained Gradient for Unified XAI
Won Jun Kim, Hyungjin Chung, Jaemin Kim +3
Gradient-based methods are a prototypical family of explainability techniques, especially for image-based models. Nonetheless, they have several shortcomings in that they (1) requi…
ACDC: Autoregressive Coherent Multimodal Generation using Diffusion Correction
Hyungjin Chung, Dohun Lee, Jong Chul Ye
Autoregressive models (ARMs) and diffusion models (DMs) represent two leading paradigms in generative modeling, each excelling in distinct areas: ARMs in global context modeling an…
A Survey on Diffusion Models for Inverse Problems
Giannis Daras, Hyungjin Chung, Chieh-Hsin Lai +5
Diffusion models have become increasingly popular for generative modeling due to their ability to generate high-quality samples. This has unlocked exciting new possibilities for so…
Fundus image enhancement through direct diffusion bridges
Sehui Kim, Hyungjin Chung, Se Hie Park +3
We propose FD3, a fundus image enhancement method based on direct diffusion bridges, which can cope with a wide range of complex degradations, including haze, blur, noise, and shad…
Amortized Posterior Sampling with Diffusion Prior Distillation
Abbas Mammadov, Hyungjin Chung, Jong Chul Ye
We propose Amortized Posterior Sampling (APS), a novel variational inference approach for efficient posterior sampling in inverse problems. Our method trains a conditional flow mod…
Defining Neural Network Architecture through Polytope Structures of Dataset
Sangmin Lee, Abbas Mammadov, Jong Chul Ye
Current theoretical and empirical research in neural networks suggests that complex datasets require large network architectures for thorough classification, yet the precise nature…
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction
Riccardo Barbano, Alexander Denker, Hyungjin Chung +5
Denoising diffusion models have emerged as the go-to generative framework for solving inverse problems in imaging. A critical concern regarding these models is their performance on…