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

Neural Radiance Field (NeRF), a new novel view synthesis with implicit scene
representation has taken the field of Computer Vision by storm. As a novel view synthesis …

We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D
generative models which produce a single output representation, Shap-E directly generates …

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 present a super-fast convergence approach to reconstructing the per-scene radiance
field from a set of images that capture the scene with known poses. This task, which is often …

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 …

Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

Videos show continuous events, yet most--if not all--video synthesis frameworks treat them
discretely in time. In this work, we think of videos of what they should be--time-continuous …

We introduce GAUDI, a generative model capable of capturing the distribution of complex
and realistic 3D scenes that can be rendered immersively from a moving camera. We tackle …

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 …