In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results. Unlike other existing face swapping works that only use face recognition model to keep the identity similarity, we propose 3D shape-aware identity to control the face shape with the geometric supervision from 3DMM and 3D face reconstruction method. Meanwhile, we introduce the Semantic Facial Fusion module to optimize the combination of encoder and decoder features and make adaptive blending, which makes the results more photo-realistic. Extensive experiments on faces in the wild demonstrate that our method can preserve better identity, especially on the face shape, and can generate more photo-realistic results than previous state-of-the-art methods.
翻译:在这项工作中,我们提出了一种高度忠诚的面部互换方法,称为“HifiFace ” ( HifiFace ), 它可以保存源面的面部形状,并产生光现实的结果。 与其他现有的面部互换工作不同,这些工作只使用面部识别模型来保持身份相似性。 我们提出3D形状认知身份,用3DMM和3D面部重建方法的几何监督来控制面部形状。 与此同时,我们引入了“语义模糊融合模块 ”, 以优化编码器和解码器功能的组合,并进行适应性混合,从而使结果更具有光现实性。 野生面部的广泛实验表明,我们的方法可以保持更好的身份,特别是在面部形状上,并能够产生比以往最先进的方法更多的光现实效果。