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 …

This paper presents a 3D diffusion model that automatically generates 3D digital avatars
represented as neural radiance fields (NeRFs). A significant challenge for 3D diffusion is …

A recent trend in generative modeling is building 3D-aware generators from 2D image
collections. To induce the 3D bias, such models typically rely on volumetric rendering, which …

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 …

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 …

We present a StyleGAN2‐based deep learning approach for 3D shape generation, called
SDF‐StyleGAN, with the aim of reducing visual and geometric dissimilarity between …

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 …

Mesh generation is of great value in various applications involving computer graphics and
virtual content, yet designing generative models for meshes is challenging due to their …

Diffusion models have emerged as a powerful tool for point cloud generation. A key
component that drives the impressive performance for generating high-quality samples from …