OmniEdit: A Training-free framework for Lip Synchronization and Audio-Visual Editing

TL;DR

OmniEdit introduces a training-free, stable, and effective framework for lip synchronization and audio-visual editing, reducing computational costs and data needs by reformulating the editing process and eliminating stochastic elements.

Contribution

It proposes a novel training-free approach that replaces traditional fine-tuning, enabling efficient and robust audio-visual editing without additional training.

Findings

Effective lip synchronization demonstrated

Robust audio-visual editing achieved

Reduces computational overhead

Abstract

Lip synchronization and audio-visual editing have emerged as fundamental challenges in multimodal learning, underpinning a wide range of applications, including film production, virtual avatars, and telepresence. Despite recent progress, most existing methods for lip synchronization and audio-visual editing depend on supervised fine-tuning of pre-trained models, leading to considerable computational overhead and data requirements. In this paper, we present OmniEdit, a training-free framework designed for both lip synchronization and audio-visual editing. Our approach reformulates the editing paradigm by substituting the edit sequence in FlowEdit with the target sequence, yielding an unbiased estimation of the desired output. Moreover, by removing stochastic elements from the generation process, we establish a smooth and stable editing trajectory. Extensive experimental results validate the effectiveness and robustness of the proposed framework. Code is available at https://github.com/l1346792580123/OmniEdit.