5 citations · 12 across the 17 of their papers we have counts for
19 papers
FlowLong: Inference-time Long Video Generation via Manifold-constrained Tweedie Matching
Jangho Park, Geon Yeong Park, Gihyun Kwon +1
Extending the generation horizon of video diffusion models to long sequences remains a long-standing and important challenge. Existing training-free approaches fall into two catego…
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…
Improving Video Diffusion Transformer Training by Multi-Feature Fusion and Alignment from Self-Supervised Vision Encoders
Dohun Lee, Hyeonho Jeong, Jiwook Kim +2
Video diffusion models have advanced rapidly in the recent years as a result of series of architectural innovations (e.g., diffusion transformers) and use of novel training objecti…
Reangle-A-Video: 4D Video Generation as Video-to-Video Translation
Hyeonho Jeong, Suhyeon Lee, Jong Chul Ye
We introduce Reangle-A-Video, a unified framework for generating synchronized multi-view videos from a single input video. Unlike mainstream approaches that train multi-view video…
Inference-Time Diffusion Model Distillation
Geon Yeong Park, Sang Wan Lee, Jong Chul Ye
Diffusion distillation models effectively accelerate reverse sampling by compressing the process into fewer steps. However, these models still exhibit a performance gap compared to…
Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation
Hyeonho Jeong, Chun-Hao Paul Huang, Jong Chul Ye +2
While recent foundational video generators produce visually rich output, they still struggle with appearance drift, where objects gradually degrade or change inconsistently across…
Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems
Hyungjin Chung, Jong Chul Ye
Recent inverse problem solvers that leverage generative diffusion priors have garnered significant attention due to their exceptional quality. However, adaptation of the prior is n…
CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models
Hyungjin Chung, Jeongsol Kim, Geon Yeong Park +2
Classifier-free guidance (CFG) is a fundamental tool in modern diffusion models for text-guided generation. Although effective, CFG has notable drawbacks. For instance, DDIM with C…
LDMol: A Text-to-Molecule Diffusion Model with Structurally Informative Latent Space Surpasses AR Models
Jinho Chang, Jong Chul Ye
With the emergence of diffusion models as a frontline generative model, many researchers have proposed molecule generation techniques with conditional diffusion models. However, th…
Ground-A-Score: Scaling Up the Score Distillation for Multi-Attribute Editing
Hangeol Chang, Jinho Chang, Jong Chul Ye
Despite recent advancements in text-to-image diffusion models facilitating various image editing techniques, complex text prompts often lead to an oversight of some requests due to…
Spectral Motion Alignment for Video Motion Transfer using Diffusion Models
Geon Yeong Park, Hyeonho Jeong, Sang Wan Lee +1
The evolution of diffusion models has greatly impacted video generation and understanding. Particularly, text-to-video diffusion models (VDMs) have significantly facilitated the cu…
Generalized Consistency Trajectory Models for Image Manipulation
Beomsu Kim, Jaemin Kim, Jeongsol Kim +1
Diffusion models (DMs) excel in unconditional generation, as well as on applications such as image editing and restoration. The success of DMs lies in the iterative nature of diffu…
DreamMotion: Space-Time Self-Similar Score Distillation for Zero-Shot Video Editing
Hyeonho Jeong, Jinho Chang, Geon Yeong Park +1
Text-driven diffusion-based video editing presents a unique challenge not encountered in image editing literature: establishing real-world motion. Unlike existing video editing app…
DreamSampler: Unifying Diffusion Sampling and Score Distillation for Image Manipulation
Jeongsol Kim, Geon Yeong Park, Jong Chul Ye
Reverse sampling and score-distillation have emerged as main workhorses in recent years for image manipulation using latent diffusion models (LDMs). While reverse diffusion samplin…
Latent Inversion with Timestep-aware Sampling for Training-free Non-rigid Editing
Yunji Jung, Seokju Lee, Tair Djanibekov +2
Text-guided non-rigid editing involves complex edits for input images, such as changing motion or compositions within their surroundings. Since it requires manipulating the input s…
VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models
Hyeonho Jeong, Geon Yeong Park, Jong Chul Ye
Text-to-video diffusion models have advanced video generation significantly. However, customizing these models to generate videos with tailored motions presents a substantial chall…
Regularization by Texts for Latent Diffusion Inverse Solvers
Jeongsol Kim, Geon Yeong Park, Hyungjin Chung +1
The recent development of diffusion models has led to significant progress in solving inverse problems by leveraging these models as powerful generative priors. However, challenges…
Prompt-tuning latent diffusion models for inverse problems
Hyungjin Chung, Jong Chul Ye, Peyman Milanfar +1
We propose a new method for solving imaging inverse problems using text-to-image latent diffusion models as general priors. Existing methods using latent diffusion models for inver…
Ground-A-Video: Zero-shot Grounded Video Editing using Text-to-image Diffusion Models
Hyeonho Jeong, Jong Chul Ye
Recent endeavors in video editing have showcased promising results in single-attribute editing or style transfer tasks, either by training text-to-video (T2V) models on text-video…