  The aim of this work is learning to reshape the object in an input image to
an arbitrary new shape, by just simply providing a single reference image with
an object instance in the desired shape. We propose a new Generative
Adversarial Network (GAN) architecture for such an object reshaping problem,
named ReshapeGAN. The network can be tailored for handling all kinds of problem
settings, including both within-domain (or single-dataset) reshaping and
cross-domain (typically across mutiple datasets) reshaping, with paired or
unpaired training data. The appearance of the input object is preserved in all
cases, and thus it is still identifiable after reshaping, which has never been
achieved as far as we are aware. We present the tailored models of the proposed
ReshapeGAN for all the problem settings, and have them tested on 8 kinds of
reshaping tasks with 13 different datasets, demonstrating the ability of
ReshapeGAN on generating convincing and superior results for object reshaping.
To the best of our knowledge, we are the first to be able to make one GAN
framework work on all such object reshaping tasks, especially the cross-domain
tasks on handling multiple diverse datasets. We present here both ablation
studies on our proposed ReshapeGAN models and comparisons with the
state-of-the-art models when they are made comparable, using all kinds of
applicable metrics that we are aware of.
