We introduce DMTet, a deep 3D conditional generative model that can synthesize high-
resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits …

Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

Neural signed distance functions (SDFs) are emerging as an effective representation for 3D
shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural …

In this work, we propose a novel technique to generate shapes from point cloud data. A point
cloud can be viewed as samples from a distribution of 3D points whose density is …

This paper presents a new approach for 3D shape generation, enabling direct generative
modeling on a continuous implicit representation in wavelet domain. Specifically, we …

In this work, we explore the challenging task of generating 3D shapes from text. Beyond the
existing works, we propose a new approach for text-guided 3D shape generation, capable of …

Abstract Multi-Layer Perceptrons (MLPs) make powerful functional representations for
sampling and reconstruction problems involving low-dimensional signals like images …

We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of
a category and generating dense correspondences among shapes. With DIF, a 3D shape is …

Most existing geometry processing algorithms use meshes as the default shape
representation. Manipulating meshes, however, requires one to maintain high quality in the …

Generative models aim to learn the distribution of observed data by generating new
instances. With the advent of neural networks, deep generative models, including variational …