The field of visual computing is rapidly advancing due to the emergence of generative
artificial intelligence (AI), which unlocks unprecedented capabilities for the generation …

Diffusion Models (DMs) have demonstrated state-of-the-art performance in content
generation without requiring adversarial training. These models are trained using a two-step …

Diffusion models currently achieve state-of-the-art performance for both conditional and
unconditional image generation. However, so far, image diffusion models do not support …

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

Although the recent rapid evolution of 3D generative neural networks greatly improves 3D
shape generation, it is still not convenient for ordinary users to create 3D shapes and control …

We introduce MeshGPT a new approach for generating triangle meshes that reflects the
compactness typical of artist-created meshes in contrast to dense triangle meshes extracted …

Probabilistic diffusion models have achieved state-of-the-art results for image synthesis,
inpainting, and text-to-image tasks. However, they are still in the early stages of generating …

In this work, we focus on synthesizing high-quality textures on 3D meshes. We present Point-
UV diffusion, a coarse-to-fine pipeline that marries the denoising diffusion model with UV …

We present Dictionary Fields, a novel neural representation which decomposes a signal into
a product of factors, each represented by a classical or neural field representation, operating …

We present a cascaded diffusion model based on a part-level implicit 3D representation. Our
model achieves state-of-the-art generation quality and also enables part-level shape editing …