Numerical models of surface tension play an increasingly important role in our capacity to
understand and predict a wide range of multiphase flow problems. The accuracy and …

Non‐rigid registration computes an alignment between a source surface with a target
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …

In this paper, we present TEXTure, a novel method for text-guided generation, editing, and
transfer of textures for 3D shapes. Leveraging a pretrained depth-to-image diffusion model …

We introduce a new general-purpose approach to deep learning on three-dimensional
surfaces based on the insight that a simple diffusion layer is highly effective for spatial …

Few prior works study deep learning on point sets. PointNet is a pioneer in this direction.
However, by design PointNet does not capture local structures induced by the metric space …

We present a new deep learning approach for matching deformable shapes by introducing
Shape Deformation Networks which jointly encode 3D shapes and correspondences. This is …

We report here on recent developments and advances in pore-scale X-ray tomographic
imaging of subsurface porous media. Our particular focus is on immiscible multi-phase fluid …

We introduce a new framework for learning dense correspondence between deformable 3D
shapes. Existing learning based approaches model shape correspondence as a labelling …

We present a novel representation of maps between pairs of shapes that allows for efficient
inference and manipulation. Key to our approach is a generalization of the notion of map …

It has been recently shown that neural networks can recover the geometric structure of a
face from a single given image. A common denominator of most existing face geometry …