TLDR: Object-AVEdit is a new model for object-level audio-visual editing, allowing precise addition, replacement, and removal of objects and their sounds in videos. It achieves this through a novel audio generation model with word-to-sounding-object alignment and a holistically optimized inversion-regeneration algorithm for structural preservation and high-quality results. The model demonstrates superior performance in both audio and video editing tasks, offering advanced control for video post-production and filmmaking.
The demand for sophisticated audio-visual editing in fields like video post-production and filmmaking is rapidly growing. While many models exist for general audio or video editing, they often fall short when it comes to precise, object-level manipulations. Imagine wanting to remove a specific dog and its bark from a scene, or replace them with a pig and its sounds, all while leaving the rest of the background visuals and audio completely untouched. This level of granular control has been a significant challenge for existing technologies.
A new research paper introduces Object-AVEdit, a novel model designed to tackle this very problem by enabling object-level audio-visual editing. This innovative approach allows users to perform operations such as adding, replacing, or removing specific objects and their associated sounds within a video, all while maintaining the original structural integrity of the unedited parts of the scene.
How Object-AVEdit Works
Object-AVEdit operates on an “inversion-regeneration” principle. This means it first analyzes the original audio-visual data, effectively “inverting” it into a foundational representation, and then “regenerates” the edited version based on user instructions. The key to its success lies in two major advancements.
Firstly, the researchers developed a unique audio generation model. Unlike previous audio models that lacked precise control over individual “sounding objects,” Object-AVEdit’s audio model creates a clear link between specific words in a text description and the corresponding sounds in the audio. This breakthrough allows for object-level attention control during audio editing, making it possible to isolate and manipulate sounds associated with particular objects.
Secondly, to ensure that edits are seamless and preserve the original scene’s structure, the team proposed an “inversion-regeneration holistically-optimized editing algorithm.” This algorithm is meticulously designed to ensure that no crucial information is lost during the initial inversion process and that the subsequent regeneration produces high-quality, natural-looking and sounding results. It focuses on both retaining the original context and achieving superior editing effects simultaneously.
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Editing Capabilities and Performance
The model supports three fundamental object-level editing tasks: object addition, object replacement, and object removal. For instance, you could add rain and its sound to a scene with a dog, replace a cat with a dog in a classroom, or remove a yellow dog from a tiled floor. The system handles both the visual and auditory aspects of these changes with fine semantic alignment.
Extensive experiments have shown that Object-AVEdit achieves advanced results in both audio and video object-level editing tasks. It outperforms existing single-modality editing models in terms of relevance to target edits, structural consistency, inter-frame consistency, and visual quality. Furthermore, the newly developed audio generation model itself demonstrates superior performance in generating high-quality audio that is semantically aligned with text prompts.
This technology holds immense potential for real-world applications in video editing with sound, including professional filmmaking, short-form video production, and various post-production workflows, offering unprecedented precision and control to creators. For more technical details, you can refer to the full research paper here: Object-AVEdit: An Object-Level Audio-Visual Editing Model.
