OmniEdit: A Training-free framework for Lip Synchronization and Audio-Visual Editing
This addresses a fundamental challenge in multimodal learning for applications like film production and virtual avatars, but it appears incremental as it builds on existing editing paradigms.
The paper tackles the problem of lip synchronization and audio-visual editing by introducing OmniEdit, a training-free framework that avoids supervised fine-tuning, resulting in reduced computational overhead and data requirements while demonstrating effectiveness and robustness in experiments.
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.