8 citations · 11 across the 14 of their papers we have counts for
16 papers
GeoFix: Geometry Fixing for 4D Consistent Generation
Yeobin Hong, Hyojun Go, Dohun Lee +7
Video diffusion models increasingly serve as priors for 4D generation, but the generated videos often lack geometric consistency. Recent approaches address this through post-traini…
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…
Aligning Few-Step Generative Models by Amortizing Sample-based Variational Inference
Jaewoo Lee, Hyeongyu Kang, Dohyun Kim +9
Aligning a few-step generative model is challenging, since existing alignment frameworks typically rely on restrictive assumptions: a tractable likelihood, a specific ODE/SDE solve…
MeshReGen: A Unified 3D Geometry Regeneration Framework
Geon Yeong Park, Roman Shapovalov, Rakesh Ranjan +3
We consider the problem of regenerating 3D objects from 2D images and initial 3D shapes. Most 3D generators operate in a one-shot fashion, converting text or images to a 3D object…
Memory-V2V: Memory-Augmented Video-to-Video Diffusion for Consistent Multi-Turn Editing
Dohun Lee, Chun-Hao Paul Huang, Xuelin Chen +3
Video-to-video diffusion models achieve impressive single-turn editing performance, but practical editing workflows are inherently iterative. When edits are applied sequentially, e…
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…
TweedieMix: Improving Multi-Concept Fusion for Diffusion-based Image/Video Generation
Gihyun Kwon, Jong Chul Ye
Despite significant advancements in customizing text-to-image and video generation models, generating images and videos that effectively integrate multiple personalized concepts re…
VideoGuide: Improving Video Diffusion Models without Training Through a Teacher's Guide
Dohun Lee, Bryan S Kim, Geon Yeong Park +1
Text-to-image (T2I) diffusion models have revolutionized visual content creation, but extending these capabilities to text-to-video (T2V) generation remains a challenge, particular…
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…
Unified Editing of Panorama, 3D Scenes, and Videos Through Disentangled Self-Attention Injection
Gihyun Kwon, Jangho Park, Jong Chul Ye
While text-to-image models have achieved impressive capabilities in image generation and editing, their application across various modalities often necessitates training separate m…
Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models
Gihyun Kwon, Simon Jenni, Dingzeyu Li +3
While there has been significant progress in customizing text-to-image generation models, generating images that combine multiple personalized concepts remains challenging. In this…
Patch-wise Graph Contrastive Learning for Image Translation
Chanyong Jung, Gihyun Kwon, Jong Chul Ye
Recently, patch-wise contrastive learning is drawing attention for the image translation by exploring the semantic correspondence between the input and output images. To further ex…
Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing
Hyelin Nam, Gihyun Kwon, Geon Yeong Park +1
With the remarkable advent of text-to-image diffusion models, image editing methods have become more diverse and continue to evolve. A promising recent approach in this realm is De…
ED-NeRF: Efficient Text-Guided Editing of 3D Scene with Latent Space NeRF
Jangho Park, Gihyun Kwon, Jong Chul Ye
Recently, there has been a significant advancement in text-to-image diffusion models, leading to groundbreaking performance in 2D image generation. These advancements have been ext…