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MIVE: The Future of Video Editing

MIVE simplifies video editing with precise control and advanced techniques.

Samuel Teodoro, Agus Gunawan, Soo Ye Kim, Jihyong Oh, Munchurl Kim

― 6 min read


MIVE: Game Changer in MIVE: Game Changer in Editing precision and ease. MIVE revolutionizes video edits with
Table of Contents

Video editing is the process of arranging and modifying video footage to create a new version of the content. This can include cutting scenes, adding effects, or inserting new elements. With the rise of technology, especially artificial intelligence (AI), editing videos has become faster and easier. People can now use simple text prompts to tell the software what changes they want, making video editing as easy as asking for a pizza delivery.

The Challenge of Editing Multiple Instances

When it comes to editing videos, things can get tricky, especially when multiple objects need changes. Imagine you have a video where you want to swap a red car for a blue one while ensuring the trees in the background remain untouched. Traditional editing methods often change more than intended, leading to funny mishaps—like turning the sky purple instead of just changing the car's color! This problem is known in the tech world as editing leakage.

Enter MIVE: A New Solution

Recognizing the need for better video editing tools, a new framework called MIVE was created. Think of MIVE as your friendly neighborhood handyman, equipped with smart tools designed to handle multiple edits at once. The goal of MIVE is to make sure each object in a video can be edited without messing up others. This means users can make specific changes while keeping everything else intact—just like altering a sandwich order without changing the entire menu.

How MIVE Works

MIVE uses two main techniques to achieve clean edits. The first technique makes sure that the edits are separate from one another, which prevents one change from spilling over into other areas. The second technique focuses on improving accuracy, ensuring that when you ask for an edit, it goes exactly where you want it to go. It’s like having a super precise painter who knows to only color within the lines.

Creating the MIVE Dataset

For MIVE to learn and work effectively, it needs training data—lots of it! So, a new dataset, called the MIVE Dataset, was created. This dataset contains various videos, each featuring different objects and instances, like a treasure chest filled with colorful gems just waiting to be explored. The MIVE Dataset helps MIVE understand and learn how to make edits more accurately, ensuring it can handle video scenarios efficiently.

Measuring Success with CIA

To determine how well MIVE does its job, a new scoring system called the Cross-Instance Accuracy (CIA) Score was introduced. Think of the CIA score as a report card for how well MIVE keeps edits from leaking into other areas. The higher the score, the better MIVE is at keeping everything in check, ensuring that the edits look good and don’t cause any surprises.

Testing MIVE

Once MIVE was all set up, it went through extensive testing—kind of like an athlete getting ready for a big competition. MIVE was put head-to-head with other video editing methods, and the results were impressive. It outperformed the competition in several areas, particularly in keeping edits accurate and preventing leakage. It's like winning a game of dodgeball while ensuring no one trips over the balls on the court!

User Experience

To see how people felt about MIVE, a user study was conducted. Participants were shown edited videos from MIVE and other methods and were asked which ones they preferred. They looked at various factors such as how smooth the video looked, how well the edits aligned with the target captions, and how much editing leakage occurred. MIVE shone in these tests, with users often picking it as their favorite tool for its overall effectiveness. It’s like being the most popular kid in school because of your awesome skills!

The Importance of Local Editing

Editing videos is not just about making broad changes; it's also about the little details. Local video editing focuses on making precise cuts and edits in specific areas without altering the whole scene. This is where MIVE really excels. It can tweak individual objects—say, giving a character a new hat—without changing the entire scene, allowing for a layered editing approach that maintains the video's integrity.

The Technical Side of MIVE

MIVE achieves its impressive editing abilities through a series of complex technical processes. It uses a unique sampling method that ensures each edit remains distinct. The beauty of MIVE is that it does not need extra training with every new edit type. It can adapt on the fly, which is a game changer in the video editing world.

The Future of Video Editing with MIVE

As MIVE and similar technologies continue to evolve, the future of video editing is likely to become even more streamlined and user-friendly. The barriers to entering the world of video editing are coming down. With tools like MIVE, people from all walks of life can create content that looks professional without needing extensive training or experience—much like how anyone can pick up a smartphone and capture moments.

Common Use Cases

  1. Social Media: People love sharing their lives online. MIVE can help users create eye-catching videos for platforms like Instagram and TikTok by making quick edits without hassle.

  2. Content Creation: YouTubers and content creators can use MIVE to polish their videos, ensuring that they look their best without spending hours on post-production.

  3. Learning and Education: Educators can create engaging video lessons, altering footage to highlight key points or removing unnecessary distractions, making learning more enjoyable.

  4. Marketing: Businesses can generate promotional videos, using MIVE to quickly adapt their content to different audiences or campaigns without starting from scratch.

  5. Personal Projects: From family videos to wedding highlights, MIVE allows anyone to give their cherished memories a proper touch-up without becoming a video editing expert.

Conclusion

MIVE represents a significant step forward in video editing technology. With its ability to manage multiple edits simultaneously and maintain accuracy, it opens up new avenues for creativity and expression. Users can now craft videos that align with their visions while keeping the editing process straightforward. Just like how a trusty toolbox can make DIY projects a breeze, MIVE brings ease and precision to video editing.

So, whether you’re a casual user looking to enhance your social media presence or a professional content creator aiming for that perfect cut, MIVE is your go-to digital assistant for all things video. The future of video editing just got a lot brighter, and who knows? With tools like MIVE, we might all be just one clever prompt away from becoming the next Spielberg!

Original Source

Title: MIVE: New Design and Benchmark for Multi-Instance Video Editing

Abstract: 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 a zero-shot $\textbf{M}$ulti-$\textbf{I}$nstance $\textbf{V}$ideo $\textbf{E}$diting framework, called MIVE. MIVE is a general-purpose mask-based framework, not dedicated to specific objects (e.g., people). MIVE introduces two key modules: (i) Disentangled Multi-instance Sampling (DMS) to prevent editing leakage and (ii) Instance-centric Probability Redistribution (IPR) to ensure precise localization and faithful editing. Additionally, we present our new MIVE Dataset 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 MIVE 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. The project page is available at https://kaist-viclab.github.io/mive-site/

Authors: Samuel Teodoro, Agus Gunawan, Soo Ye Kim, Jihyong Oh, Munchurl Kim

Last Update: 2024-12-17 00:00:00

Language: English

Source URL: https://arxiv.org/abs/2412.12877

Source PDF: https://arxiv.org/pdf/2412.12877

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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