81 citations · 189 across the 8 of their papers we have counts for
16 papers
Novel View Synthesis with Diffusion Models
Daniel Watson, William Chan, Ricardo Martin-Brualla +3
We present 3DiM, a diffusion model for 3D novel view synthesis, which is able to translate a single input view into consistent and sharp completions across many views. The core com…
HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields
Keunhong Park, Utkarsh Sinha, Peter Hedman +5
Neural Radiance Fields (NeRF) are able to reconstruct scenes with unprecedented fidelity, and various recent works have extended NeRF to handle dynamic scenes. A common approach to…
FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling
Christopher Xie, Keunhong Park, Ricardo Martin-Brualla +1
We investigate the use of Neural Radiance Fields (NeRF) to learn high quality 3D object category models from collections of input images. In contrast to previous work, we are able…
StEP: Style-based Encoder Pre-training for Multi-modal Image Synthesis
Moustafa Meshry, Yixuan Ren, Larry S Davis +1
We propose a novel approach for multi-modal Image-to-image (I2I) translation. To tackle the one-to-many relationship between input and output domains, previous works use complex tr…
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields
Jonathan T. Barron, Ben Mildenhall, Matthew Tancik +3
The rendering procedure used by neural radiance fields (NeRF) samples a scene with a single ray per pixel and may therefore produce renderings that are excessively blurred or alias…
IBRNet: Learning Multi-View Image-Based Rendering
Qianqian Wang, Zhicheng Wang, Kyle Genova +6
We present a method that synthesizes novel views of complex scenes by interpolating a sparse set of nearby views. The core of our method is a network architecture that includes a m…