5 citations · 6 across the 12 of their papers we have counts for
14 papers
OmniXRef: A Unified Framework for Multimodal Multi-Reference Video Editing
Xiaoyan Cong, Tianyu Wang, Hyeonho Jeong +7
Creative intent in video editing can be expressed through text instructions (e.g., "remove the people"), image references (e.g., subject appearance), video references (e.g., camera…
Rebalancing Reference Frame Dominance to Improve Motion in Image-to-Video Models
Wooseok Jeon, Seungho Park, Seunghyun Shin +3
Image-to-video models often generate videos that remain overly static, compared to text-to-video models. While prior approaches mitigate this issue by weakening or modifying the im…
EM-Vid: Training-Free Entity-Centric Memory for Efficient and Consistent Multi-Shot Video Generation
Jente Vandersanden, Matheus Gadelha, Chun-Hao P. Huang +2
Multi-shot video generation requires maintaining a consistent appearance of recurring entities across shots while remaining faithful to shot-specific text prompts. Recent autoregre…
TrajectoryMover: Generative Movement of Object Trajectories in Videos
Kiran Chhatre, Hyeonho Jeong, Yulia Gryaditskaya +3
Generative video editing has enabled creative control over an object's position in a video by prescribing an object's 3D or 2D motion trajectory, while preserving both video plausi…
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…
SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and Time
Zhening Huang, Hyeonho Jeong, Xuelin Chen +4
We present SpaceTimePilot, a video diffusion model that disentangles space and time for controllable generative rendering. Given a monocular video, SpaceTimePilot can independently…
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…
JOG3R: Towards 3D-Consistent Video Generators
Chun-Hao Paul Huang, Niloy Mitra, Hyeonho Jeong +2
Emergent capabilities of image generators have led to many impactful zero- or few-shot applications. Inspired by this success, we investigate whether video generators similarly exh…
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…
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…
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…
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…
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…