  We propose Neural Crossbreed, a feed-forward neural network that can learn a
semantic change of input images in a latent space to create the morphing
effect. Because the network learns a semantic change, a sequence of meaningful
intermediate images can be generated without requiring the user to specify
explicit correspondences. In addition, the semantic change learning makes it
possible to perform the morphing between the images that contain objects with
significantly different poses or camera views. Furthermore, just as in
conventional morphing techniques, our morphing network can handle shape and
appearance transitions separately by disentangling the content and the style
transfer for rich usability. We prepare a training dataset for morphing using a
pre-trained BigGAN, which generates an intermediate image by interpolating two
latent vectors at an intended morphing value. This is the first attempt to
address image morphing using a pre-trained generative model in order to learn
semantic transformation. The experiments show that Neural Crossbreed produces
high quality morphed images, overcoming various limitations associated with
conventional approaches. In addition, Neural Crossbreed can be further extended
for diverse applications such as multi-image morphing, appearance transfer, and
video frame interpolation.
