Publication
arXiv
Stage
Preprint
What we read
Summary of the paper
Authors
Adheesh Sunil Juvekar, Onkar Kishor Susladkar, Kiet A. Nguyen, Muntasir Wahed, Nabeel Bashir, Xiaona Zhou, Tianjiao Yu, Vedant Shah, Ismini Lourentzou
Universities and research institutions
University of Illinois Urbana-Champaign, Muntasir Wahed, Nabeel Bashir, Xiaona Zhou, Tianjiao Yu, Vedant Shah, and Ismini Lourentzou

Institution metadata: Original paper ↗

What they did and found

EditVid uses memory of the previous frame and smart matching to apply edits across a video without building new models. It keeps appearance and timing steady, but it does not cover all motion changes.

Why it matters

Editors can work without new models for each task; it keeps subjects and scenes steady, but motion control is not guaranteed.

EditVid uses memory of the last frame with careful matching to keep edits steady over time. It can change color or style while keeping motion and background stable, but object movement may still vary.

In practice, you get solid subject-preserving edits without new models, but limits appear when edits require changing how objects move or interact with the scene.

Read the paper PDF ↗

Original sources · 1
  1. One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing ↗arXiv · 2026-09-03

Check the original paper for its authors, methods, version and access terms.

What remains uncertain

Limitations noted include edits that alter object dynamics (speed, trajectory, motion direction) being outside the current scope; robustness depends on reliable correspondences and temporal memory, which can fail under heavy occlusion or large appearance changes.