Recent AI-based video editing has enabled users to edit videos through simple text prompts, significantly simplifying the editing process. However, recent zero-shot video editing techniques primarily focus on global or single-object edits, which can lead to unintended changes in other parts of the video. When multiple objects require localized edits, existing methods face challenges, such as unfaithful editing, editing leakage, and lack of suitable evaluation datasets and metrics. To overcome these limitations, we propose Probability Redistribution for Instance-aware Multi-object Video Editing (PRIMEdit). PRIMEdit is a zero-shot framework that introduces two key modules: (i) Instance-centric Probability Redistribution (IPR) to ensure precise localization and faithful editing and (ii) Disentangled Multi-instance Sampling (DMS) to prevent editing leakage. Additionally, we present our new MIVE Dataset for video editing featuring diverse video scenarios, and introduce the Cross-Instance Accuracy (CIA) Score to evaluate editing leakage in multi-instance video editing tasks. Our extensive qualitative, quantitative, and user study evaluations demonstrate that PRIMEdit significantly outperforms recent state-of-the-art methods in terms of editing faithfulness, accuracy, and leakage prevention, setting a new benchmark for multi-instance video editing.

We also present our new MIVE Dataset specifically designed for multi-instance video editing tasks. MIVE Dataset features 200 diverse videos sourced from the VIPSeg dataset.

We generated and summarized the source captions using LLaVa and Llama 3, respectively. We then manually inserted tags in the source captions to establish instance-to-mask correspondence. Finally, we generated the target edit captions using Llama 3. We show a sample input video and source and target captions below. The target instance captions are color-coded to match the color of the masks.