An Object is Worth 64x64 Pixels: Generating 3D Object via Image
  Diffusion
Authors: name: Xingguang Yan, name: Han-Hung Lee, name: Ziyu Wan, name: Angel X. Chang

We introduce a new approach for generating realistic 3D models with UV maps
through a representation termed "Object Images." This approach encapsulates
surface geometry, appearance, and patch structures within a 64x64 pixel image,
effectively converting complex 3D shapes into a more manageable 2D format. By
doing so, we address the challenges of both geometric and semantic irregularity
inherent in polygonal meshes. This method allows us to use image generation
models, such as Diffusion Transformers, directly for 3D shape generation.
Evaluated on the ABO dataset, our generated shapes with patch structures
achieve point cloud FID comparable to recent 3D generative models, while
naturally supporting PBR material generation.