SyncEdit: Rethinking Lip Synchronization as Editing with Audio-Driven Diffusion Models

arXiv:2603.09084 · cs.CV · Submitted 2026-03-10 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "SyncEdit: Rethinking Lip Synchronization as Editing with Audio-Driven Diffusion Models".

Jane: Lip synchronization and audio-visual editing are fundamental challenges in multimodal learning, underpinning applications like film production and virtual avatars.

Tom: First, who's behind it and why it matters.

Title and authors: Tom: So, to get into the specifics of 'SyncEdit: Rethinking Lip Synchronization as Editing with Audio-Driven Diffusion Models', we see that it's by Lin, Jin, and Zhang from HiThink Research at the University of Science and Technology of China and Zhejiang University.

Jane: That team has clearly been working in a space where they want to push the boundaries of how audio and video interact using diffusion models. The authors are proposing this new way to handle lip synchronization that moves away from traditional methods that rely on extensive supervised fine-tuning.

Lu: Their focus on 'rethinking' suggests they are addressing some deep structural limitations in how we currently train these models to achieve perfect alignment, which is a very insightful starting point for further theoretical exploration.

Meng: The authors mention that existing approaches often require large-scale paired audio-visual datasets, and this paper aims to bypass that hurdle entirely by offering a training-free framework. That's what caught my attention from an engineering side—less data dependency is always a win for deployment speed.

Lalam: From a cultural perspective, if we can reduce the barrier of needing massive datasets for creative tasks like virtual avatars, it democratizes content creation immensely, allowing more people to participate in making high-quality digital media.

The paper's summary: Tom: The core summary of 'SyncEdit: Rethinking Lip Synchronization as Editing with Audio-Driven Diffusion Models' is that they introduce OmniEdit, which is a training-free framework for both lip synchronization and audio-visual editing.

Jane: Essentially, the big idea is to change the way we think about editing; instead of starting with an edit sequence, they reformulate it to use the target sequence directly. This supposedly yields an unbiased estimation of what we actually want as our final output.

Lu: That shift in paradigm—from editing a path to iterating over a desired result—is mathematically quite elegant and suggests a clearer way to define the underlying dynamical system that governs these audio-visual transformations.

Meng: The paper also emphasizes that by eliminating random noise from the generation process, they establish a deterministic and smooth trajectory for the editing process, which addresses stability issues we often see in iterative methods.

Lalam: Eliminating stochastic elements definitely speaks to robustness; a stable trajectory means less jitter and more reliable results when generating content for applications like virtual avatars or film production.

The paper's improvements: Tom: One of the key improvements they propose is replacing the traditional iterative process with one that iterates directly over the target sequence, which they claim "more accurately approximates the underlying ODE" than iterating over an edit sequence.

Jane: That means instead of a complex optimization step to find an edit path, you follow a simpler sequence defined by what you want to achieve at each time step. This makes the whole process much more interpretable for those trying to understand how the model is working.

Lu: Their specific formulation involves defining the initialization of the target trajectory differently than in earlier methods, starting directly from a point derived from both the source video and noise, which they claim provides an unbiased estimate of that desired quantity.

Meng: That unbiased estimation part is critical because it means you don't introduce systematic errors just by how you start the process, which simplifies things for our engineers trying to debug the generation quality.

Lalam: If we can achieve a result that is both stable and accurately reflects the target intention without relying on heavy training, it really streamlines the entire development lifecycle for any application we build using these diffusion models.

Conclusion: Tom: So, to wrap up this discussion on 'SyncEdit: Rethinking Lip Synchronization as Editing with Audio-Driven Diffusion Models', they've essentially given us OmniEdit, a training-free method that uses the target sequence instead of an edit sequence for lip sync and editing.

Jane: That really boils down to a framework that aims to be both unbiased in its estimation and very stable because it removes the random noise that usually causes inconsistencies during generation.

Lu: It feels like they've successfully mapped out a clearer route through the diffusion process, which is valuable for anyone trying to build more sophisticated audio-visual tools based on these models.

Meng: From a practical standpoint, this means we might see a faster path to creating high-quality synchronized media without needing huge amounts of labeled data for every single specific task.

Lalam: I think the biggest impact here is how it moves us toward more user-friendly and reliable multimodal generation tools that don't require specialized training for every new use case.

Lixiang Lin, Siyuan Jin, Jinshan Zhang

HiThink Research · University of Science and Technology of China · Zhejiang University

cs.CV

Submitted: 2026-03-10

Updated: 2026-09-28

Code: https://github.com/l1346792580123/OmniEdit

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 77/100

The gist: Lip synchronization and audio-visual editing are fundamental challenges in multimodal learning, underpinning applications like film production and virtual avatars.

Key concepts

Edit Paradigm Reformulation
The core idea is changing how the editing process is viewed. Instead of starting with a sequence of edits (like FlowEdit), the framework substitutes a target sequence directly into the model's flow. This yields an unbiased estimate of what you want, removing reliance on pre-defined edit sequences.
Target Sequence Iteration
The iterative process is redefined to loop over the desired target sequence rather than an edit sequence. This structural change makes the underlying mathematical system clearer and easier to analyze, leading to a more accurate approximation of the true desired output trajectory.
Stochastic Noise Elimination
Random noise injection in generation causes inconsistent temporal errors. SyncEdit replaces this with an estimated noise formulation inspired by other methods. This replacement ensures smoother iteration dynamics and prevents error accumulation, resulting in a deterministic and visually stable editing trajectory.

Terminology

Summary

Lip synchronization and audio-visual editing are fundamental challenges in multimodal learning, underpinning applications like film production and virtual avatars. The gist: OmniEdit presents a training-free framework for lip synchronization and audio-visual editing by reformulating the edit paradigm to use the target sequence instead of an edit sequence, yielding an unbiased estimation of the desired output while eliminating stochastic elements from the generation process to establish a smooth and stable editing trajectory.

OmniEdit Framework Overview

OmniEdit is a training-free framework designed for both lip synchronization and audio-visual editing, leveraging pre-trained audio-to-video diffusion models and audio-visual foundation models to achieve precise alignment without requiring task-specific fine-tuning or large paired datasets. The core innovation involves reformulating the editing paradigm by substituting the edit sequence in FlowEdit with the target sequence, yielding an unbiased estimation of the desired output. This approach directly addresses limitations of existing methods that depend on supervised fine-tuning or computationally intensive optimization.

Target Sequence Iteration Strategy

The framework redefines the iterative process by transforming it into an iteration over the target sequence, which yields a clearer interpretation of the underlying dynamical system and improves its analytical tractability. The formulation involves defining sequences such as:

  1. The initialization of the target trajectory is defined as Xtar tmax = (1 − tmax)Xsrc + tmaxϵ.

  2. The continuous-time ODE of the edit sequence is solved using a numerical ODE solver via time discretization, with "ti" representing a monotonically decreasing time schedule.

  3. This reformulation results in an unbiased estimate of the desired target quantity because the initialization of the target trajectory is defined differently than in the original edit sequence, which starts directly from Xedit tmax = Xsrc.

Stochastic Noise Elimination for Stability

To enhance stability and quality, OmniEdit eliminates stochastic elements from the generation process. The paper notes that sampling a random Gaussian noise at each iteration produces a non-smooth trajectory, which propagates temporal inconsistency. Instead of this, the framework replaces random noise injection with an estimated noise formulation inspired by Noise-Level Guidance [27] and FlowCycle [42]. Specifically, the source sequence update is derived from the previous iteration's estimated noise:

(12) Xsrc ti−1 = (1 − ti−1)Xsrc + ti−1ϵ.

This modification results in smoother iteration dynamics and alleviate error accumulation associated with random noise, leading to a deterministic and smooth generation trajectory.

Application in Lip Synchronization

When applied to lip synchronization, OmniEdit is directly applicable to pre-trained audio-to-video diffusion models. The algorithm synthesizes a target video that preserves the visual identity and dynamics of the source while conforming to a target audio signal Atar. Key procedural steps include:

  1. Estimating Gaussian noise and constructing the initial noisy target state Xtar tmax = (1−tmax)Xsrc + tmaxϵ via linear interpolation between the source video and noise.

  2. Iteratively traversing the process using coupled updates where V tar ti ← Vθ(Xtar ti, Atar ti, ti, c).

This method achieves performance comparable to state-of-the-art methods like OmniSync on benchmarks such as HDTF and AIGCLipSync Benchmark.

Application in Audio-Visual Editing

OmniEdit extends naturally to prompt-driven audio–visual editing by leveraging a pre-trained audio–visual foundation model. Textual prompts serve as high-level semantic guidance, enabling coherent manipulation of both visual and auditory modalities. The framework simultaneously performs visual and auditory modifications, ensuring that both the generated target video and audio are coherently aligned with the intended semantic and perceptual attributes, such as age, gender, emotion, behaviors, or car categories. This unified formulation allows for cross-modal generation tasks like synthesizing video from audio or generating audio from video.

Ablation Study Findings

The ablation study validates the framework's contributions by comparing different components. Iterating over the edit sequence is shown to produce results that fails to yield an unbiased estimate of the target, whereas iterating over the target sequence more accurately approximates the underlying ODE, leading to improved generative quality as reflected by lower FID and FVD scores. Furthermore, replacing stochastic noise with estimated noise produces a smoother trajectory, resulting in sharper and more clearly defined dental details compared to methods that use randomly sampled Gaussian noise. This confirms that the proposed design improves both fidelity and visual clarity.

Conclusion

OmniEdit provides a practical and efficient alternative to conventional fine-tuning-based editing methods by offering a training-free formulation for lip synchronization and audio-visual editing.

Improvements for AI systems

Based on the provided paper, here are specific, actionable improvements for existing AI systems and what those improved systems can achieve:

) Improvements for Existing AI Systems:

  1. [Lip Synchronization & Audio-Visual Editing Frameworks]: Replace current supervised fine-tuning methods (which require large paired datasets and significant computational overhead) with the OmniEdit framework.

  2. [Training Paradigm]: Transition from task-specific fine-tuning to a training-free paradigm by leveraging pre-trained audio-to-video diffusion models and audio–visual foundation models directly.

  3. [Editing Trajectory Stability]: Eliminate stochastic Gaussian sampling in the generation process and replace it with noise estimated from the pre-trained diffusion model, resulting in a deterministic and smooth editing trajectory.

  4. [Editing Paradigm Reformulation]: Reframe the iterative refinement of the edit sequence (as done in FlowEdit) by substituting it with an iteration scheme defined directly over the target sequence, yielding an unbiased estimation of the desired output.

  5. [Bias Reduction in Editing]: Utilize a target iterative sequence initialization that differs from standard methods (e.g., initializing from a specific noise realization) to obtain an unbiased estimate of the desired target quantity, thereby reducing inherent bias introduced by sequential editing.

) Capabilities of the Improved AI System:

  1. [High-Fidelity Lip Synchronization]: The improved system can achieve precise mouth-movement alignment with speech audio while preserving the visual identity and dynamics of a source video (e.g., using OmniEdit for lip synchronization on HDTF).

  2. [Flexible Audio–Visual Editing]: The system can perform concurrent, prompt-driven manipulation of both visual content and audio signals according to textual prompts, allowing users to control attributes such as age, gender, emotion, behavior, and even car categories in a temporally synchronized and semantically consistent manner (e.g., using OmniEdit for LTX-2).

  3. [Efficient Content Creation]: The framework enables plug-and-play multimodal content creation without requiring task-specific fine-tuning or the collection of large-scale paired audio–visual datasets, significantly reducing data and computational requirements for deploying these capabilities.

  4. [Enhanced Visual Quality in Editing]: By iterating over the target sequence and using estimated noise, the system produces outputs with superior visual clarity, specifically yielding sharper and more clearly defined dental structures compared to methods that iterate over the edit sequence or use random noise injection (as demonstrated by Fig. 3).

  5. [Robustness to Complex Scenarios]: The framework demonstrates strong robustness under complex conditions, such as occlusions and profile views during lip synchronization.

Abstract

Lip synchronization refers to the task of modifying facial lip movements such that they are temporally aligned with a given audio signal. It is a fundamental problem in audio-visual synthesis for generating realistic talking-head videos. Recent progress in diffusion-based generative models has led to remarkable advances in lip synchronization. Nevertheless, existing methods typically achieve audio-visual alignment by fine-tuning pre-trained diffusion models on large scale datasets, resulting in substantial computational costs and significant data requirements. Inspired by FlowEdit, we reformulate lip synchronization as a video editing problem. Building upon a pre-trained audio-driven diffusion model, our approach achieves lip synchronization in a training-free manner, without additional fine-tuning or paired data. In this paper, we present SyncEdit, a training-free framework designed for lip synchronization. We reformulate the editing paradigm by substituting the edit sequence in FlowEdit with the target sequence, yielding an unbiased estimation of the desired output. Moreover, we propose annealed noise alignment, which progressively alignes the sampled Gaussian noise with diffusion-model-estimated noise during iterative editing, producing 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/SyncEdit here.

Sources

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