# OmniEdit: A Training-free framework for Lip Synchronization and Audio-Visual Editing Source: [OmniEdit: A Training-free framework for Lip Synchronization and Audio-Visual Editing]() Collection index: [llms.txt]() Data updated: 2026-03-12T05:00:04.726Z Authors: Lixiang Lin, Siyuan Jin, Jinshan Zhang Published: 2026-03-10T01:51:28.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Lip Synchronization on HDTF](): Lip synchronization; HDTF; FID: 7.623. - [Lip Synchronization on AIGC-LipSync](): Lip synchronization; AIGC-LipSync; FID: 9.663. ## Original sources and implementation [Original paper]() [PDF]()