One Editor, Many Edits:
A Unified Training-free Framework for Diverse Video Editing

Adheesh Sunil Juvekar, Onkar Kishor Susladkar, Kiet A. Nguyen, Muntasir Wahed, Nabeel Bashir, Xiaona Zhou, Tianjiao Yu, Vedant Shah, Ismini Lourentzou
PLAN Lab University of Illinois Urbana-Champaign
Paper arXiv GitHub Comparisons
TL;DRWe introduce EditVid, a training-free framework for diverse video editing, supporting various instruction-guided and reference-guided edits in one framework, including style transfer, attribute modification, object insertion, part-level editing, and subject replacement.
SourceEᴅɪᴛVɪᴅ
Attribute edit

“Change the elephant's color to blue.”

SourceEᴅɪᴛVɪᴅ
Global tone

“Convert the video to black and white.”

SourceEᴅɪᴛVɪᴅ
Environment appearance

“Change the turquoise waves to black.”

SourceEᴅɪᴛVɪᴅ
Style transfer

“Convert the video style to ukiyo-e.”

SourceEᴅɪᴛVɪᴅ
Dynamic phenomenon

“Change the color of the fire to blue.”

SourceEᴅɪᴛVɪᴅ
Attribute edit

“Change the black clothing to red.”

Overview

Abstract

Video editing spans diverse editing paradigms, yet achieving high-quality instruction-guided and subject-guided editing within a single unified framework remains challenging. We introduce EditVid, a training-free framework combining sparse causal memory for local coherence, correspondence-based post-attention token injection for long-range identity preservation, and soft latent blending for edit locality. The same framework supports instruction-guided and reference-guided edits, including style transfer, attribute modification, object insertion, part-level editing, and subject replacement. On FiVE, EditVid achieves 78.16 FiVE-Acc, compared with 58.95 for the strongest evaluated training-free baseline, while obtaining competitive results on IVEBench. A user study further shows a 51.8% overall preference for EditVid over 7 competing methods.

Method

A local–global decomposition for temporal control

EᴅɪᴛVɪᴅ operates only on visual latent tokens inside frozen MM-DiT-based image editors. Text and optional reference-image conditioning remain unchanged, preserving each editor's original semantic control.

EᴅɪᴛVɪᴅ architecture: sparse causal memory, correspondence-based post-attention token injection, and soft latent blending within frozen MM-DiT-based image editors
EᴅɪᴛVɪᴅ combines adjacent-frame spatio-temporal attention, correspondence-based post-attention token injection, and soft latent blending in frozen MM-DiT-based image editors.
01

Sparse causal memory

Each frame attends to visual key–value states from the immediately preceding frame. This bounded context gives strong short-range coherence while avoiding unreliable long-range RoPE interactions.

02

Post-attention token injection

Confidence- and cycle-consistent correspondences match each frame to an anchor. Matched visual representations are injected after attention for long-range appearance and identity preservation.

03

Soft latent blending

Timestep-dependent preservation weights are derived from source–edit trajectory discrepancies, retaining source content where needed while allowing the requested edit elsewhere.

Quantitative evaluation

Results

EᴅɪᴛVɪᴅ is evaluated on fine-grained editing accuracy, traditional preservation metrics, IVEBench, and curated subject-guided and general video-editing sets.

Table 1: quantitative comparison on FiVE editing accuracy
Table 1 · FiVE editing accuracy
Table 2: quantitative comparison on FiVE traditional metrics
Table 2 · FiVE traditional metrics
Table 3: quantitative evaluation on IVEBench and curated video-editing sets
Table 3 · IVEBench and curated-set evaluation
Qualitative evaluation

Comparisons with training-free baselines

Select an edit to compare synchronized outputs from the source, representative baselines, and EᴅɪᴛVɪᴅ. Click any video to pause or resume the complete row.

Editing instruction
Citation

BibTeX

@article{juvekar2026editvid,
  title   = {One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing},
  author  = {Adheesh Sunil Juvekar and Onkar Kishor Susladkar and Kiet A. Nguyen and Muntasir Wahed and Nabeel Bashir and Xiaona Zhou and Tianjiao Yu and Vedant Shah and Ismini Lourentzou},
  journal = {arXiv preprint arXiv:2609.04190},
  year    = {2026},
}