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 real-time, 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.

翻译：我们提出ShaDDR，一种基于示例的深度生成神经网络，该方法通过对输入粗体素形状进行几何细节化与条件纹理生成，产生高分辨率带纹理的3D形状。在少量带细节和纹理的示例形状上训练后，我们的方法通过多分辨率体素上采样学习几何细节化，并利用可微渲染在体素表面生成纹理，该渲染过程基于来自少数视角的示例纹理图像。生成过程为实时操作，生成体素分辨率高达512^3的3D模型耗时不足1秒。生成的形状保留了输入粗体素模型的整体结构，同时可通过学习到的隐编码操控几何细节与纹理的风格。实验表明，与先前工作相比，我们的方法能生成更高分辨率的形状，且具有合理且改进的几何细节与清晰纹理。此外，我们展示了该方法从真实世界照片重建的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建模应用，以证明该方法对各类用户输入的泛化能力及提供的可控性——用户可通过交互式雕刻粗体素形状来定义细节化3D模型的整体结构。