Improving 3D shape generation by shape space refinement and details recovery

In this work we discuss two novel perspectives to improve 3D shape generation. The first perspective is to improve the local rigidity of the shape space in shape generation from a latent vector, while the second perspective is to use patch copy to do details recovery in shape completion. In the first improvement, we introduce an unsupervised loss for training parametric deformation shape generators. The key idea is to enforce the preservation of local rigidity among the generated shapes. Our approach builds on an approximation of the as-rigid-as possible (or ARAP) deformation energy. In the second improvement, we introduce a data-driven shape completion approach that focuses on completing geometric details of missing regions of 3D shapes. Our key insight is to copy and deform the patches from the partial input to complete the missing regions. This enables us to preserve the style of local geometric features, even if it is drastically different from the training data.Computer Science