12 papers with code • 0 benchmarks • 1 datasets
Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from i) time-consuming optimization for projecting real images to the latent vectors, ii) or inaccurate embedding through an encoder.
Inspired by the recent progress in implicit neural representation, we propose to formulate the guided super-resolution as a neural implicit image interpolation problem, where we take the form of a general image interpolation but use a novel Joint Implicit Image Function (JIIF) representation to learn both the interpolation weights and values.
GLICO learns a mapping from the training examples to a latent space and a generator that generates images from vectors in the latent space.
Image interpolation, or image morphing, refers to a visual transition between two (or more) input images.
In this work, we propose an approach to perform non-uniform image interpolation based on a Gaussian Mixture Model.