We propose a novel learnable representation for detail-preserving shape deformation extending a traditional cage-based deformation technique. We demonstrate the utility of our method for synthesizing shape variations and deformation transfer.
We propose a high-fidelity differentiable renderer for point clouds. We demonstrate how the proposed technique can be used to leverage contemporary deep neural networks to achieve state-of-the-art results in challenging geometry processing tasks.
Representing and generating shapes using neural networks
We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super-resolution techniques, we …
Making use of this extremely flexible yet unstructured form of shape represenation.