Rendering results of synthesized textures conditioned on the shape. For each example, we vary the shape, shown for 4 different views, over the columns (i.e., fixed z) and the texture over the rows (i.e., different z).
We present a method for decorating existing 3D shape collections by learning a tex- ture generator from internet photo collections. We condition the StyleGAN  texture generation by injecting multiview silhouettes of a 3D shape with SPADE-IN . To bridge the inherent domain gap between the multiview silhouettes from the shape col- lection and the distribution of silhouettes in the photo collection, we employ a mixture of silhouettes from both collections for training. Furthermore, we do not assume each exemplar in the photo collection is viewed from more than one vantage point, and lever- age multiview discriminators to promote semantic view-consistency over the generated textures. We verify the efficacy of our design on three real-world 3D shape collections.
GAN, Texture generation
Paper and video
Trained model and code
Pieter Peers was supported in part by NSF grant IIS-1909028.