O-DisCo-Edit: Object Distortion Control for Unified Realistic Video Editing

Yuqing Chen, Junjie Wang, Lin Liu, Ruihang Chu, Xiaopeng Zhang +2 more
9/1/2025
cs.CVcs.AI

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Abstract

Diffusion models have recently advanced video editing, yet controllable editing remains challenging due to the need for precise manipulation of diverse object properties. Current methods require different control signal for diverse editing tasks, which complicates model design and demands significant training resources. To address this, we propose O-DisCo-Edit, a unified framework that incorporates a novel object distortion control (O-DisCo). This signal, based on random and adaptive noise, flexibly encapsulates a wide range of editing cues within a single representation. Paired with a "copy-form" preservation module for preserving non-edited regions, O-DisCo-Edit enables efficient, high-fidelity editing through an effective training paradigm. Extensive experiments and comprehensive human evaluations consistently demonstrate that O-DisCo-Edit surpasses both specialized and multitask state-of-the-art methods across various video editing tasks. https://cyqii.github.io/O-DisCo-Edit.github.io/

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Cite this paper

@article{chen2025odiscoedit,
  title  = {O-DisCo-Edit: Object Distortion Control for Unified Realistic Video Editing},
  author = {Yuqing Chen and Junjie Wang and Lin Liu and Ruihang Chu and Xiaopeng Zhang and Qi Tian and Yujiu Yang},
  year   = {2025},
  eprint = {2509.01596},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.01596v1}
}

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