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Synthesizing novel 3D models that resemble the input example has long beenpursued by researchers and artists in computer graphics. In this paper, wepresent Sin3DM, a diffusion model that learns the internal patch distributionfrom a single 3D textured shape and generates high-quality variations with finegeometry and texture details. Training a diffusion model directly in 3D wouldinduce large memory and computational cost. Therefore, we first compress theinput into a lower-dimensional latent space and then train a diffusion model onit. Specifically, we encode the input 3D textured shape into triplane featuremaps that represent the signed distance and texture fields of the input. Thedenoising network of our diffusion model has a limited receptive field to avoidoverfitting, and uses triplane-aware 2D convolution blocks to improve theresult quality. Aside from randomly generating new samples, our model alsofacilitates applications such as retargeting, outpainting and local editing.Through extensive qualitative and quantitative evaluation, we show that ourmodel can generate 3D shapes of various types with better quality than priormethods.

Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape