3d

Geometric details in 3D shapes is a defining factor in many industries such as AR/VR, VFX and design. However, creating high-fidelity …

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 …

Making use of this extremely flexible yet unstructured form of shape represenation.