We present FlashLips, a two-stage, mask-free lip-sync system that decouples lips control from rendering and achieves real-time performance running at over 100 FPS on a single GPU, while matching the visual quality of larger state-of-the-art models. Stage 1 is a compact, one-step latent-space editor that reconstructs an image using a reference identity, a masked target frame, and a low-dimensional lips-pose vector, trained purely with reconstruction losses - no GANs or diffusion. To remove explicit masks at inference, we use self-supervision: we generate mouth-altered variants of the target image, that serve as pseudo ground truth for fine-tuning, teaching the network to localize edits to the lips while preserving the rest. Stage 2 is an audio-to-pose transformer trained with a flow-matching objective to predict lips-poses vectors from speech. Together, these stages form a simple and stable pipeline that combines deterministic reconstruction with robust audio control, delivering high perceptual quality and faster-than-real-time speed.

翻译：本文提出FlashLips，一种两阶段、免掩码的唇形同步系统。该系统将唇部控制与渲染过程解耦，在单GPU上实现超过100 FPS的实时性能，同时视觉质量媲美更大的先进模型。第一阶段是一个紧凑的单步潜在空间编辑器，它利用参考身份、掩码目标帧和低维唇部姿态向量重建图像，其训练完全基于重建损失——无需GAN或扩散模型。为在推理时消除显式掩码，我们采用自监督方法：生成目标图像的嘴部变化版本作为伪真值进行微调，从而教会网络将编辑操作定位在唇部区域并保持其余部分不变。第二阶段是一个基于流匹配目标训练的音频到姿态Transformer，用于从语音预测唇部姿态向量。这两个阶段共同构成一个简单稳定的流程，将确定性重建与鲁棒的音频控制相结合，实现了高感知质量和超实时速度。