3DGEN: A GAN-based approach for generating novel 3D models from image
  data
Antoine Schnepf Flavian Vasile Ugo Tanielian
The recent advances in text and image synthesis show a great promise for the
future of generative models in creative fields. However, a less explored area
is the one of 3D model generation, with a lot of potential applications to game
design, video production, and physical product design. In our paper, we present
3DGEN, a model that leverages the recent work on both Neural Radiance Fields
for object reconstruction and GAN-based image generation. We show that the
proposed architecture can generate plausible meshes for objects of the same
category as the training images and compare the resulting meshes with the
state-of-the-art baselines, leading to visible uplifts in generation quality.