Neural Implicit Morphing of Face Images
CVPR 2024(2023)
摘要
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.
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