Paper Detail
Paul Hyunbin Cho, Jinhyuk Jang, SeokYoung Lee, Joungbin Lee, Siyoon Jin, Heeseong Shin, Jung Yi, Yunjin Park, Chulmin Park, Seungryong Kim
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.
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@article{cho2026lip,
title = {Lip Forcing: Few-Step Autoregressive Diffusion for Real-time Lip Synchronization},
author = {Paul Hyunbin Cho and Jinhyuk Jang and SeokYoung Lee and Joungbin Lee and Siyoon Jin and Heeseong Shin and Jung Yi and Yunjin Park and Chulmin Park and Seungryong Kim},
year = {2026},
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 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 tw},
url = {https://arxiv.org/abs/2606.11180},
keywords = {cs.CV, diffusion models, lip synchronization, video-to-video, bidirectional attention, denoising steps, causal students, teacher-student distillation, inference-time CFG, SyncNet, time-to-first-frame, code available, huggingface daily},
eprint = {2606.11180},
archiveprefix = {arXiv},
}
{}