GRNEdit：从二元证据视角实现高效通用视频编辑。

Computer Science > Computer Vision and Pattern Recognition

arXiv:2608.16328 (cs)

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[Submitted on 17 Aug 2026]

Title:GRNEdit: Efficient General Video Editing from a New Binary-Evidence Perspective in Generative Refinement Networks

Authors:Feng Xie, Jiagao Hu, Fuhao Li, Zepeng Wang, Yuxuan Chen, Dahua Gao, Fei Wang, Daiguo Zhou
View a PDF of the paper titled GRNEdit: Efficient General Video Editing from a New Binary-Evidence Perspective in Generative Refinement Networks, by Feng Xie and 7 other authors

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Abstract:Instruction-based general video editing seeks to unify diverse editing operations within a single, intuitive interface. Existing approaches often rely on resource-intensive conditioning, using either heavyweight branches or costly source concatenation. Is there any efficient way to model editing intent? Thus, we introduce GRNEdit, a lightweight two-stage framework. GRN inspires our approach by encoding visual semantics through combinations of bits. Through task-specific fine-tuning, we take this representation further and recast editing semantics as local retain-or-flip decisions over individual bits. Source information is consequently modeled as coordinate-wise evidence supporting the observed binary states, while the GRN backbone remains responsible for resolving their global composition into coherent generative semantics. In Stage I, a compact encoder translates discrete source codes into continuous evidence signals, which GRN assimilates throughout binary refinement. Inspired by null-prompt training for classifier-free guidance, we further assign the null condition an editing-specific meaning: an empty instruction denotes no edit and is supervised through source reconstruction. This identity pathway not only implicitly strengthens evidence utilization and content preservation in Stage I, but also produces a source-preserving state in the same representation space as the edited state. Stage II can therefore directly compare each edited state with its source-preserving counterpart and use their discrepancy to revise unresolved target-bit decisions. Trained on only 0.6M pairs with less than 3\% conditioning parameters, GRNEdit-2B and GRNEdit-8B achieve scores of 4.03 and 4.18 on OpenVE-Bench. The 2B model outperforms multiple 14B open-source editors, while the 8B model performs on par with leading open-source editors.

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as:
arXiv:2608.16328 [cs.CV]

 
(or
arXiv:2608.16328v1 [cs.CV] for this version)

 
https://doi.org/10.48550/arXiv.2608.16328

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arXiv-issued DOI via DataCite (pending registration)

Submission history
From: Feng Xie [view email]
[v1]
Mon, 17 Aug 2026 09:31:56 UTC (11,822 KB)

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