Diffusion models have emerged as the state-of-the-art for image generation, among other
tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural …

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

Automatically generating high-quality real world 3D scenes is of enormous interest for
applications such as virtual reality and robotics simulation. Towards this goal, we introduce …

Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-
trained 2D image diffusion models and training directly on 3D shapes. When training a …

The field of neural rendering has witnessed significant progress with advancements in
generative models and differentiable rendering techniques. Though 2D diffusion has …

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 …

We present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D
meshes generation from a single unposed image in the wild using both2D and 3D priors. In …

Recent work has shown the possibility of training generative models of 3D content from 2D
image collections on small datasets corresponding to a single object class, such as human …

Abstract Recent Diffusion Transformers (ie, DiT) have demonstrated their powerful
effectiveness in generating high-quality 2D images. However, it is unclear how the …

In this work, we introduce CC3D, a conditional generative model that synthesizes complex
3D scenes conditioned on 2D semantic scene layouts, trained using single-view images …