ABSTRACT A common need for artificial intelligence models in the broader geoscience is to
encode various types of spatial data, such as points, polylines, polygons, graphs, or rasters …

Denoising diffusion models (DDMs) have shown promising results in 3D point cloud
synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high …

We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for
generative diffusion models. Our shape representation can encode 3D shapes given as …

Abstract We present TexFusion (Texture Diffusion), a new method to synthesize textures for
given 3D geometries, using only large-scale text-guided image diffusion models. In contrast …

Many applications of text generation require incorporating different constraints to control the
semantics or style of generated text. These constraints can be hard (eg, ensuring certain …

Time series forecasting has been a widely explored task of great importance in many
applications. However, it is common that real-world time series data are recorded in a short …

Vision transformer networks have shown superiority in many computer vision tasks. In this
paper, we take a step further by proposing a novel generative vision transformer with latent …

We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists
of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k …

We propose a 3D shape generation framework (SDF-Diffusion in short) that uses denoising
diffusion models with continuous 3D representation via signed distance fields (SDF). Unlike …

We propose a new representation for encoding 3D shapes as neural fields. The
representation is designed to be compatible with the transformer architecture and to benefit …