AlphaFace实现高保真实时换脸，对极端表情姿态更鲁棒。
AlphaFace: High Fidelity and Real-time Face Swapper Robust to Facial Pose
- 用视觉与文本语义对比损失增强身份表征
- 在极端姿态下换脸质量显著优于现有方法
- 保持实时性能，适合需要高精度的应用
现有换脸方法在约束条件下表现良好，但在处理极端面部姿态时质量明显下降。虽有方法引入显式几何特征提升鲁棒性，但会增加计算开销和依赖。基于扩散的方法虽效果出色，却难以实现实时处理。本文提出AlphaFace，利用开源视觉-语言模型及CLIP的图像与文本嵌入，设计新颖的视觉与文本语义对比损失，强化身份表示并精确保留属性特征，同时维持实时性能。在FF++、MPIE和LPFF数据集上的全面实验表明，AlphaFace在姿态挑战场景下超越当前最优方法。项目代码已公开于https://github.com/andrewyu90/Alphaface_Official.git。
原文摘要 · Abstract (English)
Existing face-swapping methods often deliver competitive results in constrained settings but exhibit substantial quality degradation when handling extreme facial poses. To improve facial pose robustness, explicit geometric features are applied, but this approach remains problematic since it introduces additional dependencies and increases computational cost. Diffusion-based methods have achieved remarkable results; however, they are impractical for real-time processing. We introduce AlphaFace, which leverages an open-source vision-language model and CLIP image and text embeddings to apply novel visual and textual semantic contrastive losses. AlphaFace enables stronger identity representation and more precise attribute preservation, all while maintaining real-time performance. Comprehensive experiments across FF++, MPIE, and LPFF demonstrate that AlphaFace surpasses state-of-the-art methods in pose-challenging cases. The project is publicly available on `https://github.com/andrewyu90/Alphaface_Official.git'.
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