4 citations · 5 across the 9 of their papers we have counts for
10 papers
VideoMaMa++: Temporally Consistent Video Matting via Preserve-and-Refine Token
Sangbeom Lim, Seoung Wug Oh, Heeji Yoon +2
Diffusion-based video matting has recently emerged as a leading approach, leveraging video diffusion priors from internet-scale pretraining to achieve strong zero-shot generalizati…
ViTAL-X: Video-Text Alignment with Cross-Modal Temporal Edits
Sethuraman T, Savya Khosla, Onkar Kishor Susladkar +6
Video-text models adapted from image-text architectures (e.g., CLIP) frequently exhibit temporal blindness, the inability to perceive fundamental cues like order, direction, and mo…
HarmoVid: Relightful Video Portrait Harmonization
Jun Myeong Choi, Jae Shin Yoon, Luchao Qi +2
We present a method for harmonizing the lighting of a foreground video to match a target background scene, adjusting shadows, color tone, and illumination intensity (relightful har…
VideoMaMa: Mask-Guided Video Matting via Generative Prior
Sangbeom Lim, Seoung Wug Oh, Jiahui Huang +3
Generalizing video matting models to real-world videos remains a significant challenge due to the scarcity of labeled data. To address this, we present Video Mask-to-Matte Model (V…
Tuning-Free Multi-Event Long Video Generation via Synchronized Coupled Sampling
Subin Kim, Seoung Wug Oh, Jui-Hsien Wang +2
While recent advancements in text-to-video diffusion models enable high-quality short video generation from a single prompt, generating real-world long videos in a single pass rema…
Generative Video Propagation
Shaoteng Liu, Tianyu Wang, Jui-Hsien Wang +8
Large-scale video generation models have the inherent ability to realistically model natural scenes. In this paper, we demonstrate that through a careful design of a generative vid…
Elevating Flow-Guided Video Inpainting with Reference Generation
Suhwan Cho, Seoung Wug Oh, Sangyoun Lee +1
Video inpainting (VI) is a challenging task that requires effective propagation of observable content across frames while simultaneously generating new content not present in the o…
HARIVO: Harnessing Text-to-Image Models for Video Generation
Mingi Kwon, Seoung Wug Oh, Yang Zhou +6
We present a method to create diffusion-based video models from pretrained Text-to-Image (T2I) models. Recently, AnimateDiff proposed freezing the T2I model while only training tem…
MaGGIe: Masked Guided Gradual Human Instance Matting
Chuong Huynh, Seoung Wug Oh, Abhinav Shrivastava +1
Human matting is a foundation task in image and video processing, where human foreground pixels are extracted from the input. Prior works either improve the accuracy by additional…
Putting the Object Back into Video Object Segmentation
Ho Kei Cheng, Seoung Wug Oh, Brian Price +2
We present Cutie, a video object segmentation (VOS) network with object-level memory reading, which puts the object representation from memory back into the video object segmentati…