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

Abstract We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an
object given just a single RGB image. To perform novel view synthesis in this …

Being data-driven is one of the most iconic properties of deep learning algorithms. The birth
of ImageNet drives a remarkable trend of" learning from large-scale data" in computer vision …

Neural approximations of scalar-and vector fields, such as signed distance functions and
radiance fields, have emerged as accurate, high-quality representations. State-of-the-art …

Abstract Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene
representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable …

Recent implicit neural representations have shown great results for novel view synthesis.
However, existing methods require expensive per-scene optimization from many views …

We introduce EscherNet a multi-view conditioned diffusion model for view synthesis.
EscherNet learns implicit and generative 3D representations coupled with a specialised …

Abstract Implicit Neural Representations (INR) or neural fields have emerged as a popular
framework to encode multimedia signals such as images and radiance fields while retaining …

The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …

Large image diffusion models enable novel view synthesis with high quality and excellent
zero-shot capability. However, such models based on image-to-image translation have no …