Search Results for author:
Found 6 papers, 3 papers with code
Generative Powers of Ten
We present a method that uses a text-to-image model to generate consistent content across multiple image scales, enabling extreme semantic zooms into a scene, e. g., ranging from a wide-angle landscape view of a forest to a macro shot of an insect sitting on one of the tree branches.
VQ3D: Learning a 3D-Aware Generative Model on ImageNet
Recent work has shown the possibility of training generative models of 3D content from 2D image collections on small datasets corresponding to a single object class, such as human faces, animal faces, or cars.
NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images
By rendering raw output images from the resulting NeRF, we can perform novel high dynamic range (HDR) view synthesis tasks.
Advances in Neural Rendering
The reconstruction of such a scene representation from observations using differentiable rendering losses is known as inverse graphics or inverse rendering.
IBRNet: Learning Multi-View Image-Based Rendering
Unlike neural scene representation work that optimizes per-scene functions for rendering, we learn a generic view interpolation function that generalizes to novel scenes.
Neural Reflectance Fields for Appearance Acquisition
We combine this representation with a physically-based differentiable ray marching framework that can render images from a neural reflectance field under any viewpoint and light.
