AI对口型终于能实时了，快40倍
AI对口型视频生成一直有个死结：效果好的模型太慢，没法实时。现在有人用了一种新思路——把原本需要几十步的扩散过程压缩成两步，同时把模型拆成因果的、逐块生成，结果1.3B参数的模型跑到了31帧每秒，比同规模的老模型快17.6倍；14B的大模型也比老师快39.8倍，首帧延迟不到1毫秒。关键发现是：不用分类器引导（CFG）时模型更忠于原视频长相，用了则更对准口型，他们据此设计了两步推理策略。这不是你明天能用的产品，但意味着实时视频通话、直播里的AI对口型可能很快从PPT变成现实。
📄 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.