MSEditor: Toward Consistent Multi-Shot Video Editing

Abstract

In this paper, we tackle the problem of performing consistent,unified modifications to a multi-shot video sequence. This task is par-ticularly challenging because multi-shot videos consist of discontinuoustemporal segments that vary significantly in viewpoint, camera scale, andsubject pose, leading to severe identity drift and cumulative error propa-gation. Achieving coherent edits requires establishing reliable cross-shotsemantic awareness to maintain stable subject appearance and visualcontinuity across these disjointed boundaries. To address this, we proposeMSEditor, the first framework designed specifically for consistent multi-shot video editing. To overcome the scarcity of high-quality multi-shottraining data, we repurpose existing multi-view video datasets to providerobust cross-shot supervision. Architecturally, we introduce a Supervi-sory Adapter that injects this cross-shot information into the diffusionbackbone, enabling the model to learn identity-consistent representa-tions. Furthermore, to effectively mitigate cumulative errors and ensurelong-range temporal coherence, we design a Cross-Shot Packing strat-egy that dynamically aggregates information from semantically relatedshots within the self-attention window. Extensive experiments demon-strate that MSEditor significantly outperforms existing methods on ourcurated multi-shot video editing benchmark in terms of identity preser-vation, temporal stability, and overall visual quality.