  Generative Adversarial Networks (GANs) are shown to be successful at
generating new and realistic samples including 3D object models. Conditional
GAN, a variant of GANs, allows generating samples in given conditions. However,
objects generated for each condition are different and it does not allow
generation of the same object in different conditions. In this paper, we first
adapt conditional GAN, which is originally designed for 2D image generation, to
the problem of generating 3D models in different rotations. We then propose a
new approach to guide the network to generate the same 3D sample in different
and controllable rotation angles (sample pairs). Unlike previous studies, the
proposed method does not require modification of the standard conditional GAN
architecture and it can be integrated into the training step of any conditional
GAN. Experimental results and visual comparison of 3D models show that the
proposed method is successful at generating model pairs in different
conditions.
