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，具有众多艺术与法医应用。由于姿态、光照、性别和种族的差异，该任务极具挑战性。该过程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与混合。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与灵活性。此外，我们的方法具有时间依赖性，可实现图像的连续扭曲/混合。在变形推理阶段，我们需要时间依赖扭曲的直变换和逆变换。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一映射存储于单一网络中，无需对其求逆。实验结果表明，从图像质量与人脸变形检测器角度评估，我们的方法在经典模型与生成模型间均具有竞争力。从美学角度看，所生成的图像实现了不同人脸的无缝混合，这在现有文献中尚不多见。