Learning to Generate 3D Shapes from a Single Example

Understand

Existing generative models for 3D shapes are typically trained on a large 3D dataset, often of a specific object category.

• In this paper, we investigate the deep generative model that learns from only a single reference 3D shape.
• Specifically, we present a multi-scale GAN-based model designed to capture the input shape's geometric features across a range of spatial scales.
• To avoid large memory and computational cost induced by operating on the 3D volume, we build our generator atop the tri-plane hybrid representation, which requires only 2D convolutions.