To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

We introduce MVDream, a diffusion model that is able to generate consistent multi-view
images from a given text prompt. Learning from both 2D and 3D data, a multi-view diffusion …

Modeling and re-rendering dynamic 3D scenes is a challenging task in 3D vision. Prior
approaches build on NeRF and rely on implicit representations. This is slow since it requires …

A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule
on the learned gradients, and back-propagate the score of a diffusion model through the …

Recently, DreamFusion demonstrated the utility of a pretrained text-to-image diffusion model
to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis …

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 …

We present a diffusion-based model for 3D-aware generative novel view synthesis from as
few as a single input image. Our model samples from the distribution of possible renderings …

Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV)
a reality. It requires one to generate safety critical scenarios beyond what can be collected …

We present DreamBooth3D, an approach to personalize text-to-3D generative models from
as few as 3-6 casually captured images of a subject. Our approach combines recent …