We present ShaDDR, an example-based deep generative neural network which produces a high-resolution textured 3D shape through geometry detailization and conditional texture generation applied to an input coarse voxel shape. Trained on a small set of detailed and textured exemplar shapes, our method learns to detailize the geometry via multi-resolution voxel upsampling and generate textures on voxel surfaces via differentiable rendering against exemplar texture images from a few views. The generation is interactive, taking less than 1 second to produce a 3D model with voxel resolutions up to 512^3. The generated shape preserves the overall structure of the input coarse voxel model, while the style of the generated geometric details and textures can be manipulated through learned latent codes. In the experiments, we show that our method can generate higher-resolution shapes with plausible and improved geometric details and clean textures compared to prior works. Furthermore, we showcase the ability of our method to learn geometric details and textures from shapes reconstructed from real-world photos. In addition, we have developed an interactive modeling application to demonstrate the generalizability of our method to various user inputs and the controllability it offers, allowing users to interactively sculpt a coarse voxel shape to define the overall structure of the detailized 3D shape. Code and data are available at https://github.com/qiminchen/ShaDDR.

翻译：我们提出ShaDDR，一种基于示例的深度生成神经网络，通过对输入粗糙体素形状进行几何细节化与条件纹理生成，输出高分辨率带纹理的三维形状。该方法仅需少量带细节的纹理示例形状进行训练，通过多分辨率体素上采样学习几何细节化，并利用可微分渲染将示例纹理图像（来自少数视角）映射至体素表面以生成纹理。生成过程具有交互性，可在1秒内生成分辨率高达512³体素的三维模型。生成形状保留输入粗糙体素模型的整体结构，而几何细节与纹理的风格可通过学习到的隐编码进行调控。实验表明，与现有方法相比，本方法可生成更高分辨率的三维形状，几何细节更合理且纹理更清晰。此外，我们展示了该方法从真实世界照片重建形状中学习几何细节与纹理的能力。同时，我们开发了一款交互式建模应用，验证了该方法对不同用户输入的泛化能力及其可控性——用户可通过交互式雕刻粗糙体素形状来定义细节化三维模型的整体结构。代码与数据见https://github.com/qiminchen/ShaDDR。