实时口型同步：AI视频生成快17倍
口型同步AI通常需要几十步去噪才能生成一帧，无法实时。这篇论文把14B参数的大模型蒸馏成小模型，让每个视频块只需两步去噪，速度提升17-39倍，达到31帧/秒的实时流式输出。关键发现是：不用分类器引导（CFG）时保真度好，用CFG时同步更准，于是他们设计了两步推理策略，第一步无CFG保真，第二步用CFG微调同步。结果首帧延迟低于1毫秒，远快于所有扩散基线。它不是你明天能用上的，但说明实时高质量口型同步已从不可能变成可能。
📄 原文摘要(英文)
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, 17.6times faster than its same-scale bidirectional model. The 14B student, the largest diffusion model reported for V2V lip synchronization, runs 39.8times faster than its teacher at comparable reference fidelity. Time-to-first-frame is sub-millisecond at both scales, far below every diffusion baseline.