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Lip Forcing achieves real-time video lip sync with diffusion models

By PulseAugur Editorial · [3 sources] ·

Researchers have developed "Lip Forcing," a novel autoregressive diffusion method for real-time video-to-video lip synchronization. This technique distills a large 14B parameter model into smaller, faster student models that can generate synchronized lip movements in just two denoising steps. The 1.3B parameter student model achieves real-time performance at 31 FPS, significantly outperforming previous diffusion models in speed while maintaining visual quality. AI

IMPACT Enables real-time, high-quality lip synchronization for video applications, potentially impacting content creation and virtual communication.

RANK_REASON The cluster contains a research paper detailing a new method for AI-driven lip synchronization.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Lip Forcing: Few-Step Autoregressive Diffusion for Real-time Lip Synchronization

    Autoregressive diffusion method for video-to-video lip synchronization achieves real-time performance through distillation and optimized inference schedules.

  2. arXiv cs.CV TIER_1 English(EN) · Paul Hyunbin Cho (KAIST AI), Jinhyuk Jang (KAIST AI), SeokYoung Lee (KAIST AI), Joungbin Lee (KAIST AI), Siyoon Jin (KAIST AI), Heeseong Shin (KAIST AI), Jung Yi (KAIST AI), Yunjin Park (AIPARK), Chulmin Park (AIPARK), Seungryong Kim (KAIST AI) ·

    Lip Forcing: Few-Step Autoregressive Diffusion for Real-time Lip Synchronization

    arXiv:2606.11180v1 Announce Type: new Abstract: 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 Fo…

  3. arXiv cs.CV TIER_1 English(EN) · Seungryong Kim ·

    Lip Forcing: Few-Step Autoregressive Diffusion for Real-time Lip Synchronization

    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…

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