Face morphing is a problem in computer graphics with numerous artistic and forensic applications. It is challenging due to variations in pose, lighting, gender, and ethnicity. This task consists of a warping for feature alignment and a blending for a seamless transition between the warped images. We propose to leverage coord-based neural networks to represent such warpings and blendings of face images. During training, we exploit the smoothness and flexibility of such networks by combining energy functionals employed in classical approaches without discretizations. Additionally, our method is time-dependent, allowing a continuous warping/blending of the images. During morphing inference, we need both direct and inverse transformations of the time-dependent warping. The first (second) is responsible for warping the target (source) image into the source (target) image. Our neural warping stores those maps in a single network dismissing the need for inverting them. The results of our experiments indicate that our method is competitive with both classical and generative models under the lens of image quality and face-morphing detectors. Aesthetically, the resulting images present a seamless blending of diverse faces not yet usual in the literature.

翻译：人脸变形是计算机图形学中的一个问题，具有众多艺术和法医应用。由于姿态、光照、性别和种族的差异，该任务具有挑战性。该任务包括用于特征对齐的扭曲和用于变形图像之间无缝过渡的混合。我们提出利用基于坐标的神经网络来表示人脸图像的此类扭曲和混合。在训练过程中，我们通过结合经典方法中使用的能量泛函（无需离散化）来利用此类网络的平滑性和灵活性。此外，我们的方法是时间依赖的，允许图像的连续扭曲/混合。在变形推理过程中，我们需要时间依赖扭曲的直接和逆变换。前者（后者）负责将目标（源）图像扭曲为源（目标）图像。我们的神经扭曲将这些映射存储在单个网络中，无需对其进行求逆。我们的实验结果表明，在图像质量和人脸变形检测器的角度下，我们的方法与经典模型和生成模型具有竞争力。从美学角度看，所生成的图像呈现出不同人脸的无缝混合，这在文献中尚不常见。