2 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
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…