  Novel photo-realistic texture synthesis is an important task for generating
novel scenes, including asset generation for 3D simulations. However, to date,
these methods predominantly generate textured objects in 2D space. If we rely
on 2D object generation, then we need to make a computationally expensive
forward pass each time we change the camera viewpoint or lighting. Recent work
that can generate textures in 3D requires 3D component segmentation that is
expensive to acquire. In this work, we present a novel conditional generative
architecture that we call a graph generative adversarial network (GGAN) that
can generate textures in 3D by learning object component information in an
unsupervised way. In this framework, we do not need an expensive forward pass
whenever the camera viewpoint or lighting changes, and we do not need expensive
3D part information for training, yet the model can generalize to unseen 3D
meshes and generate appropriate novel 3D textures. We compare this approach
against state-of-the-art texture generation methods and demonstrate that the
GGAN obtains significantly better texture generation quality (according to
Frechet inception distance). We release our model source code as open source.
