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Found 9 papers, 1 papers with code
Instant Continual Learning of Neural Radiance Fields
Neural radiance fields (NeRFs) have emerged as an effective method for novel-view synthesis and 3D scene reconstruction.
Efficient 3D Articulated Human Generation with Layered Surface Volumes
These layers are rendered using alpha compositing with fast differentiable rasterization, and they can be interpreted as a volumetric representation that allocates its capacity to a manifold of finite thickness around the template.
Articulated 3D Head Avatar Generation using Text-to-Image Diffusion Models
Recent work on text-guided 3D object generation has shown great promise in addressing these needs.
Generative Novel View Synthesis with 3D-Aware Diffusion Models
We present a diffusion-based model for 3D-aware generative novel view synthesis from as few as a single input image.
Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition
Capturing images is a key part of automation for high-level tasks such as scene text recognition.
Generative Neural Articulated Radiance Fields
Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress.
Fast Training of Neural Lumigraph Representations using Meta Learning
Inspired by neural variants of image-based rendering, we develop a new neural rendering approach with the goal of quickly learning a high-quality representation which can also be rendered in real-time.
ScanGAN360: A Generative Model of Realistic Scanpaths for 360$^{\circ}$ Images
Generative adversarial approaches could alleviate this challenge by generating a large number of possible scanpaths for unseen images.
Implicit Neural Representations with Periodic Activation Functions
However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations.
