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arXiv · 2606.22042

IDAG-Edit: Multi-Object Video Editing via Instance-Decoupled Attention and Guidance

Yuan-Zhih Lin·Huu-Thang Nguyen·Huu-Phu Do·Hong-Han Shuai·Ching-Chun Huang
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Abstract

Diffusion-based video editing has made significant progress; however, achieving precise and temporally consistent object-level control, especially in multi-object scenarios, remains challenging due to attention leakage, identity drift, and unstable temporal dynamics. In this work, we propose IDAGEdit, a training-free framework for fine-grained multi-object video editing with strong temporal consistency. The framework adopts Layout-guided Attention Modulation to facilitate coherent multi-object editing, while Instance-level Masks are introduced to preserve individual object identity and enforce localized attention within each object region, thereby enabling fine-grained, object-level editing. Extensive qualitative and quantitative evaluations demonstrate that our method improves temporal stability and multi-object controllability over state-of-the-art video editing approaches.

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Yuan-Zhih Lin, Huu-Thang Nguyen, Huu-Phu Do, Hong-Han Shuai, Ching-Chun Huang. 2026-06-20. IDAG-Edit: Multi-Object Video Editing via Instance-Decoupled Attention and Guidance. https://arxiv.org/abs/2606.22042

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