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 …

Neural graphics primitives, parameterized by fully connected neural networks, can be costly
to train and evaluate. We reduce this cost with a versatile new input encoding that permits …

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

We present a novel type of neural fields that uses general radial bases for signal
representation. State-of-the-art neural fields typically rely on grid-based representations for …

Probabilistic diffusion models have achieved state-of-the-art results for image synthesis,
inpainting, and text-to-image tasks. However, they are still in the early stages of generating …

Coordinate-based networks have emerged as a powerful tool for 3D representation and
scene reconstruction. These networks are trained to map continuous input coordinates to the …

Implicit neural representations store videos as neural networks and have performed well for
vision tasks such as video compression and denoising. With frame index and/or positional …

Learning-based video compression is currently a popular research topic, offering the
potential to compete with conventional standard video codecs. In this context, Implicit Neural …

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 …

Abstract Implicit Neural Representations (INRs) have emerged and shown their benefits
over discrete representations in recent years. However, fitting an INR to the given …