ComplexSync: High-Fidelity and Real-Time Lip Sync in Complex Scenarios

Diffusion-based lip synchronization models achieve strong visual quality and audio-visual alignment, but full-sequence bidirectional attention and many denoising steps make them impractical for real-time inference. We present Lip Forcing, to our knowledge the first autoregressive diffusion method for video-to-video (V2V) lip synchronization, which distills a 14B audio-conditioned bidirectional video diffusion teacher into causal students. At inference, the students generate each chunk in only two denoising steps without inference-time CFG, enabling real-time lip synchronization. A lip-sync-specific teacher-trajectory analysis reveals a CFG fidelity-sync tradeoff: no-CFG predictions favor reference fidelity, whereas CFG-guided predictions favor synchronization within a mid-trajectory band. Lip Forcing translates this finding into three analysis-derived components: Sync-Window DMD, a two-step inference schedule, and a SyncNet-based reward. We validate Lip Forcing at two student scales, both distilled from the 14B teacher. The 1.3B student crosses into real-time streaming at 31 FPS, $17.6\times$ faster than its same-scale bidirectional model. The 14B student, the largest diffusion model reported for V2V lip synchronization, runs $39.8\times$ faster than its teacher at comparable reference fidelity. Time-to-first-frame is sub-millisecond at both scales, far below every diffusion baseline.

We present HighSync, an end-to-end diffusion-based framework for high-fidelity lip synchronization that generates photorealistic talking-face videos aligned with arbitrary input audio. Existing approaches consistently struggle to reconcile image quality with synchronization accuracy, producing either visually degraded outputs or temporally inconsistent lip movements. HighSync addresses both challenges simultaneously and, to our knowledge, is the first lip sync model to operate natively at 512*512 resolution, positioning it as a viable solution for professional production environments such as the film and broadcast industries. Central to our approach is the identification and systematic elimination of a data leakage phenomenon that has silently undermined temporal modeling in prior work, preventing models from developing a genuine dependence on the audio signal. Comprehensive evaluations across both perceptual quality and synchronization accuracy metrics confirm that HighSync achieves state-of-the-art performance on both fronts. Source code, pre-trained models, and supplementary video results are publicly available at: https://github.com/saeed5959/high_sync

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 \href[]{https://github.com/l1346792580123/SyncEdit}{here}.