Neural Implicit Morphing of Face Images
Guilherme Schardong, Tiago Novello, Hallison Paz, Iurii Medvedev,, Vin\'icius da Silva, Luiz Velho, Nuno Gon\c{c}alves

TL;DR
This paper introduces a neural network-based approach for face morphing that enables smooth, continuous, and high-quality transformations, outperforming classical methods and blending diverse faces seamlessly.
Contribution
It presents a novel time-dependent neural implicit model for face morphing that simplifies warping and blending without requiring inverse transformations.
Findings
Competitive image quality compared to classical and generative models
Seamless blending of diverse faces achieved
Continuous warping allows flexible morphing transitions
Abstract
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…
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Taxonomy
TopicsFace recognition and analysis · Advanced Vision and Imaging · 3D Shape Modeling and Analysis
