39 citations · 50 across the 11 of their papers we have counts for
13 papers
Don't Throw Away the Tail: Action Upcycling for Policy Acceleration
Taesung Kwon, Jangho Park, Sunwoo Park +5
Modern robot policies predict a chunk of future actions from a single observation, execute only a prefix, and discard the rest before replanning. Choosing the length of this prefix…
Alignment-Guided Score Matching for Text-to-Image Alignment in Diffusion Models
Jaa-Yeon Lee, Yeobin Hong, Taesung Kwon +1
Diffusion models generate highly realistic images but often struggle with precise text-image alignment. While recent post-training methods improve alignment using external rewards…
Accelerating Video Inverse Problem Solvers with Autoregressive Diffusion Models
Taesung Kwon, Jonghyun Park, Hyungjin Chung +1
Diffusion models provide powerful priors for zero-shot video inverse problems, but their real-time deployment is hindered by two inefficiencies: high initial latency caused by holi…
Geometric 4D Stitching for Grounded 4D Generation
Sunwoo Park, Taesung Kwon, Jong Chul Ye
Recent 4D generation methods complete scene-level missing information using generative models and reconstruct the scene into radiance-based representations. However, these pipeline…
Reviving ConvNeXt for Efficient Convolutional Diffusion Models
Taesung Kwon, Lorenzo Bianchi, Lennart Wittke +5
Recent diffusion models increasingly favor Transformer backbones, motivated by the remarkable scalability of fully attentional architectures. Yet the locality bias, parameter effic…
Zero4D: Training-Free 4D Video Generation From Single Video Using Off-the-Shelf Video Diffusion
Jangho Park, Taesung Kwon, Jong Chul Ye
Multi-view or 4D video generation has emerged as a significant research topic. Nonetheless, recent approaches to 4D generation still struggle with fundamental limitations, as they…
VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models
Taesung Kwon, Jong Chul Ye
In this paper, we propose a novel framework for solving high-definition video inverse problems using latent image diffusion models. Building on recent advancements in spatio-tempor…
ViBiDSampler: Enhancing Video Interpolation Using Bidirectional Diffusion Sampler
Serin Yang, Taesung Kwon, Jong Chul Ye
Recent progress in large-scale text-to-video (T2V) and image-to-video (I2V) diffusion models has greatly enhanced video generation, especially in terms of keyframe interpolation. H…
Solving Video Inverse Problems Using Image Diffusion Models
Taesung Kwon, Jong Chul Ye
Recently, diffusion model-based inverse problem solvers (DIS) have emerged as state-of-the-art approaches for addressing inverse problems, including image super-resolution, deblurr…
Highly Personalized Text Embedding for Image Manipulation by Stable Diffusion
Inhwa Han, Serin Yang, Taesung Kwon +1
Diffusion models have shown superior performance in image generation and manipulation, but the inherent stochasticity presents challenges in preserving and manipulating image conte…
Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching
Kwanyoung Kim, Taesung Kwon, Jong Chul Ye
Tweedie distributions are a special case of exponential dispersion models, which are often used in classical statistics as distributions for generalized linear models. Here, we rev…
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation
Gwanghyun Kim, Taesung Kwon, Jong Chul Ye
Recently, GAN inversion methods combined with Contrastive Language-Image Pretraining (CLIP) enables zero-shot image manipulation guided by text prompts. However, their applications…
Cycle-free CycleGAN using Invertible Generator for Unsupervised Low-Dose CT Denoising
Taesung Kwon, Jong Chul Ye
Recently, CycleGAN was shown to provide high-performance, ultra-fast denoising for low-dose X-ray computed tomography (CT) without the need for a paired training dataset. Although…