通过解耦运动潜空间实现精细可控的语音驱动人脸生成
DEMO: Disentangled Motion Latent Flow Matching for Fine-Grained Controllable Talking Portrait Synthesis
- 构建解耦的运动潜空间，独立表示唇动、头部姿态和眼神
- 在多个基准上实现更逼真的视频与更好的音唇同步
- 适合需要精细动作控制的虚拟人生成场景
基于扩散模型的语音驱动人脸生成已取得显著进展，但实现时间连贯且具备细粒度运动控制仍具挑战。本文提出DEMO，一种基于流匹配的生成框架，可对唇部运动、头部姿态和眼神进行解耦、高保真控制。核心创新在于设计运动自编码器，在潜空间中实现运动因子的独立表征并近似正交化。在此解耦空间上，结合基于最优传输的流匹配与Transformer预测器，生成条件于音频的时序平滑运动轨迹。跨多个基准的大量实验表明，DEMO在视频真实感、音唇同步性和运动保真度上均优于现有方法。结果表明，将细粒度运动解耦与流基生成建模结合，为可控人脸视频合成提供了新范式。
原文摘要 · Abstract (English)
Audio-driven talking-head generation has advanced rapidly with diffusion-based generative models, yet producing temporally coherent videos with fine-grained motion control remains challenging. We propose DEMO, a flow-matching generative framework for audio-driven talking-portrait video synthesis that delivers disentangled, high-fidelity control of lip motion, head pose, and eye gaze. The core contribution is a motion auto-encoder that builds a structured latent space in which motion factors are independently represented and approximately orthogonalized. On this disentangled motion space, we apply optimal-transport-based flow matching with a transformer predictor to generate temporally smooth motion trajectories conditioned on audio. Extensive experiments across multiple benchmarks show that DEMO outperforms prior methods in video realism, lip-audio synchronization, and motion fidelity. These results demonstrate that combining fine-grained motion disentanglement with flow-based generative modeling provides a powerful new paradigm for controllable talking-head video synthesis.
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