3d neural field generation using triplane diffusion
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 …
tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural …
Instant neural graphics primitives with a multiresolution hash encoding
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 …
to train and evaluate. We reduce this cost with a versatile new input encoding that permits …
Neural fields in visual computing and beyond
Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …
computing problems using methods that employ coordinate‐based neural networks. These …
Neurbf: A neural fields representation with adaptive radial basis functions
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 …
representation. State-of-the-art neural fields typically rely on grid-based representations for …
Bacon: Band-limited coordinate networks for multiscale scene representation
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 …
scene reconstruction. These networks are trained to map continuous input coordinates to the …
Diffusion-sdf: Conditional generative modeling of signed distance functions
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 …
inpainting, and text-to-image tasks. However, they are still in the early stages of generating …
Hnerv: A hybrid neural representation for videos
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 …
vision tasks such as video compression and denoising. With frame index and/or positional …
Shacira: Scalable hash-grid compression for implicit neural representations
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 …
framework to encode multimedia signals such as images and radiance fields while retaining …
A structured dictionary perspective on implicit neural representations
G Yüce, G Ortiz-Jiménez… - Proceedings of the …, 2022 - openaccess.thecvf.com
Implicit neural representations (INRs) have recently emerged as a promising alternative to
classical discretized representations of signals. Nevertheless, despite their practical …
classical discretized representations of signals. Nevertheless, despite their practical …
Any-resolution training for high-resolution image synthesis
Generative models operate at fixed resolution, even though natural images come in a variety
of sizes. As high-resolution details are downsampled away and low-resolution images are …
of sizes. As high-resolution details are downsampled away and low-resolution images are …