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.

Sin3DM 是一种学习来自单个 3D 纹理形状的内部图案分布的扩散模型，该模型通过压缩输入到低维潜在空间来降低内存和计算成本，然后通过 Triplan 感知 2D 卷积块来训练模型，可以生成较高质量的 3D 形状，还可以用于重定位，外表绘画和局部编辑。