  We propose an unsupervised multi-conditional image generation pipeline:
cFineGAN, that can generate an image conditioned on two input images such that
the generated image preserves the texture of one and the shape of the other
input. To achieve this goal, we extend upon the recently proposed work of
FineGAN \citep{singh2018finegan} and make use of standard as well as
shape-biased pre-trained ImageNet models. We demonstrate both qualitatively as
well as quantitatively the benefit of using the shape-biased network. We
present our image generation result across three benchmark datasets-
CUB-200-2011, Stanford Dogs and UT Zappos50k.
