Abstract Three-dimensional (3D) object recognition is widely used in automated driving,
medical image analysis, virtual/augmented reality, artificial intelligence robots, and other …

Point cloud data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …

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 …

In this work, we present a novel framework built to simplify 3D asset generation for amateur
users. To enable interactive generation, our method supports a variety of input modalities …

We propose a novel approach for probabilistic generative modeling of 3D shapes. Unlike
most existing models that learn to deterministically translate a latent vector to a shape, our …

Determining whether inputs are out-of-distribution (OOD) is an essential building block for
safely deploying machine learning models in the open world. However, previous methods …

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 …

We present high quality image synthesis results using diffusion probabilistic models, a class
of latent variable models inspired by considerations from nonequilibrium thermodynamics …

Abstract We present Text2Tex, a novel method for generating high-quality textures for 3D
meshes from the given text prompts. Our method incorporates inpainting into a pre-trained …

We propose SceneTex a novel method for effectively generating high-quality and style-
consistent textures for indoor scenes using depth-to-image diffusion priors. Unlike previous …