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

Abstract 3D reconstruction of deformable (or non‐rigid) scenes from a set of monocular 2D
image observations is a long‐standing and actively researched area of computer vision and …

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 …

We propose MVDream, a multi-view diffusion model that is able to generate geometrically
consistent multi-view images from a given text prompt. By leveraging image diffusion models …

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 …

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 introduce a high resolution, 3D-consistent image and shape generation technique which
we call StyleSDF. Our method is trained on single view RGB data only, and stands on the …

Abstract 3D-aware generative adversarial networks (GANs) synthesize high-fidelity and
multi-view-consistent facial images using only collections of single-view 2D imagery …

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 …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …