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'.

翻译：现有换脸方法在受限场景下通常能取得竞争性结果，但在处理极端脸部姿态时会出现显著的质量下降。为提升对脸部姿态的鲁棒性，现有方法引入了显式几何特征，但这一方案仍存在问题，因为它引入了额外的依赖关系并增加了计算成本。基于扩散的方法已取得显著成果，然而其难以满足实时处理需求。我们提出了AlphaFace，该方法利用开源视觉语言模型以及CLIP图像与文本嵌入，并应用了新颖的视觉与文本语义对比损失。AlphaFace实现了更强的身份表征能力和更精确的属性保留，同时保持了实时性能。在FF++、MPIE和LPFF数据集上的综合实验表明，AlphaFace在姿态挑战性场景下超越了现有最先进方法。本项目已公开于 `https://github.com/andrewyu90/Alphaface_Official.git`。