Instruction-Based Video Editing

Text-to-image diffusion models have advanced video editing. However, existing video editing methods are limited by their high computational overhead and memory consumption. Furthermore, these approaches often sacrifice visual fidelity, leading to temporal inconsistencies and artifacts such as blurring and mosaic-like patterns. To address these dual challenges and strike a balance between computational efficiency and visual fidelity, we propose Edit-Your-Interest, a lightweight, text-driven, zero-shot video editing method. Edit-Your-Interest introduces a Spatio-Temporal Feature Memory bank (SFM) to cache spatial-attention features from previous frames under a fixed memory budget. The SFM maintains a representative candidate pool, reducing the computational overhead of full-sequence spatio-temporal modeling. To query this memory, we propose Feature Most-Similar Propagation (FMP), a similarity-based retrieval rule that propagates relevant cached tokens to the current frame. Together, SFM and FMP form a bounded-memory retrieval-based propagation design that improves temporal consistency while reducing blurring and mosaic-like artifacts. Finally, we introduce a feature-diversity-based SFM update algorithm to keep the memory representative and non-redundant throughout the video sequence. Furthermore, to enable precise object editing, we leverage cross-attention maps to automatically extract masks for the instances of interest. These masks are seamlessly integrated into the diffusion denoising process, enabling fine-grained control over target objects and allowing Edit-Your-Interest to perform highly accurate edits while robustly preserving the background integrity. Extensive experiments demonstrate that Edit-Your-Interest outperforms state-of-the-art methods in both efficiency and visual fidelity. Project page: https://edit-your-interest.github.io/.

Unified multimodal models have shown promising results in multimodal content generation and editing but remain largely limited to the image domain. In this work, we present UniVideo, a versatile framework that extends unified modeling to the video domain. UniVideo adopts a dual-stream design, combining a Multimodal Large Language Model (MLLM) for instruction understanding with a Multimodal DiT (MMDiT) for video generation. This design preserves the MLLM's original text generation capabilities, enables accurate interpretation of complex multimodal instructions, and maintains visual consistency in the generated content. Built on this architecture, UniVideo unifies diverse video generation and editing tasks under a single multimodal instruction paradigm and is jointly trained across them. Extensive experiments demonstrate that UniVideo matches or surpasses state-of-the-art task-specific baselines in text/image-to-video generation, in-context video generation and in-context video editing. Notably, the unified design of UniVideo enables two forms of generalization. First, UniVideo supports task composition, such as combining editing with style transfer, by integrating multiple capabilities within a single instruction. Second, even without explicit training on free-form video editing, UniVideo transfers its editing capability from large-scale image editing data to this setting, handling unseen instructions such as changing the environment or altering materials within a video. Beyond these core capabilities, UniVideo also supports visual-prompt-based video generation, where the MLLM interprets visual prompts and guides the MMDiT during synthesis. To foster future research, we released our model and code.

Recent Artificial Intelligence (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 focus on global or single-object edits, which can lead to unintended changes in other parts of the video. When editing multiple objects only within their localized regions, existing methods face challenges, such as unfaithful editing, editing leakage, and lack of suitable evaluation datasets and metrics. To overcome these limitations, we propose $\textbf{P}$robability $\textbf{R}$edistribution for $\textbf{I}$nstance-aware $\textbf{M}$ulti-object Video $\textbf{Edit}$ing ($\textbf{PRIMEdit}$). 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 the MIVE dataset for multi-instance video editing, including its subsets categorized by instance counts and sizes for controlled analysis across different levels of scene complexity. We also introduce the Cross-Instance Accuracy (CIA) Score that is firstly proposed to evaluate the editing leakage in multi-instance video editing tasks, which exhibits strong correlation with human evaluation as measured by Spearman's $ρ= 0.86$. 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.