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We present ShaDDR, an example-based deep generative neural network whichproduces a high-resolution textured 3D shape through geometry detailization andconditional texture generation applied to an input coarse voxel shape. Trainedon a small set of detailed and textured exemplar shapes, our method learns todetailize the geometry via multi-resolution voxel upsampling and generatetextures on voxel surfaces via differentiable rendering against exemplartexture images from a few views. The generation is interactive, taking lessthan 1 second to produce a 3D model with voxel resolutions up to 512^3. Thegenerated shape preserves the overall structure of the input coarse voxelmodel, while the style of the generated geometric details and textures can bemanipulated through learned latent codes. In the experiments, we show that ourmethod can generate higher-resolution shapes with plausible and improvedgeometric details and clean textures compared to prior works. Furthermore, weshowcase the ability of our method to learn geometric details and textures fromshapes reconstructed from real-world photos. In addition, we have developed aninteractive modeling application to demonstrate the generalizability of ourmethod to various user inputs and the controllability it offers, allowing usersto interactively sculpt a coarse voxel shape to define the overall structure ofthe detailized 3D shape. Code and data are available athttps://github.com/qiminchen/ShaDDR.

ShaDDR: Interactive Example-Based Geometry and Texture Generation via 3D Shape Detailization and Differentiable Rendering