Synthesizing novel 3D models that resemble the input example has long been pursued by researchers and artists in computer graphics. In this paper, we present Sin3DM, a diffusion model that learns the internal patch distribution from a single 3D textured shape and generates high-quality variations with fine geometry and texture details. Training a diffusion model directly in 3D would induce large memory and computational cost. Therefore, we first compress the input into a lower-dimensional latent space and then train a diffusion model on it. Specifically, we encode the input 3D textured shape into triplane feature maps that represent the signed distance and texture fields of the input. The denoising network of our diffusion model has a limited receptive field to avoid overfitting, and uses triplane-aware 2D convolution blocks to improve the result quality. Aside from randomly generating new samples, our model also facilitates applications such as retargeting, outpainting and local editing. Through extensive qualitative and quantitative evaluation, we show that our model can generate 3D shapes of various types with better quality than prior methods.

翻译：合成与输入示例相似的新颖三维模型一直是计算机图形学领域研究者与艺术家长期追求的目标。本文提出Sin3DM——一种扩散模型，能够从单个三维纹理形状中学习内部块分布，并生成保留精细几何细节与纹理细节的高质量变体。直接在三维空间训练扩散模型会导致巨大的内存与计算开销，因此我们首先将输入压缩至低维潜在空间，再在其上训练扩散模型。具体而言，我们将输入的三维纹理形状编码为三平面特征图，以表征输入的符号距离场与纹理场。扩散模型的去噪网络采用有限感受野以避免过拟合，并利用三平面感知的二维卷积模块提升生成质量。除随机生成新样本外，该模型还可支持重定向、外扩与局部编辑等应用。通过广泛的定性与定量评估，我们证明本模型能生成多种类型的三维形状，其质量优于现有方法。