9 papers
EditTransfer++: Toward Faithful and Efficient Visual-Prompt-Guided Image Editing
Lan Chen, Qi Mao, Yiren Song +2
Visual-prompt-guided edit transfer aims to learn image transformations directly from example pairs, offering more precise and controllable editing than purely text-driven approache…
FlowAnchor: Stabilizing the Editing Signal for Inversion-Free Video Editing
Ze Chen, Lan Chen, Yuanhang Li +1
We propose FlowAnchor, a training-free framework for stable and efficient inversion-free, flow-based video editing. Inversion-free editing methods have recently shown impressive ef…
MLV-Edit: Towards Consistent and Highly Efficient Editing for Minute-Level Videos
Yangyi Cao, Yuanhang Li, Lan Chen +1
We propose MLV-Edit, a training-free, flow-based framework that address the unique challenges of minute-level video editing. While existing techniques excel in short-form video man…
Human-Corrected Labels Learning: Enhancing Labels Quality via Human Correction of VLMs Discrepancies
Zhongnian Li, Lan Chen, Yixin Xu +2
Vision-Language Models (VLMs), with their powerful content generation capabilities, have been successfully applied to data annotation processes. However, the VLM-generated labels e…
UniVid: Unifying Vision Tasks with Pre-trained Video Generation Models
Lan Chen, Yuchao Gu, Qi Mao
Large language models, trained on extensive corpora, successfully unify diverse linguistic tasks within a single generative framework. Inspired by this, recent works like Large Vis…
Edit Transfer: Learning Image Editing via Vision In-Context Relations
Lan Chen, Qi Mao, Yuchao Gu +1
We introduce a new setting, Edit Transfer, where a model learns a transformation from just a single source-target example and applies it to a new query image. While text-based meth…