Paired 3D Model Generation with Conditional Generative Adversarial Networks
This work addresses a specific limitation in 3D model generation for computer graphics or vision applications, but it is incremental as it builds on existing conditional GAN methods without architectural changes.
The paper tackles the problem of generating the same 3D object in different rotation angles using conditional GANs, achieving success in producing paired 3D models as shown by experimental results and visual comparisons.
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.