30 citations · 123 across the 6 of their papers we have counts for
15 papers
Laser: Latent Set Representations for 3D Generative Modeling
Pol Moreno, Adam R. Kosiorek, Heiko Strathmann +6
NeRF provides unparalleled fidelity of novel view synthesis: rendering a 3D scene from an arbitrary viewpoint. NeRF requires training on a large number of views that fully cover a…
Adversarial Masking for Self-Supervised Learning
Yuge Shi, N. Siddharth, Philip H. S. Torr +1
We propose ADIOS, a masked image model (MIM) framework for self-supervised learning, which simultaneously learns a masking function and an image encoder using an adversarial object…
Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentation
Karl Stelzner, Kristian Kersting, Adam R. Kosiorek
We present ObSuRF, a method which turns a single image of a scene into a 3D model represented as a set of Neural Radiance Fields (NeRFs), with each NeRF corresponding to a differen…
NeRF-VAE: A Geometry Aware 3D Scene Generative Model
Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran +4
We propose NeRF-VAE, a 3D scene generative model that incorporates geometric structure via NeRF and differentiable volume rendering. In contrast to NeRF, our model takes into accou…
Conditional Set Generation with Transformers
Adam R Kosiorek, Hyunjik Kim, Danilo J Rezende
A set is an unordered collection of unique elements--and yet many machine learning models that generate sets impose an implicit or explicit ordering. Since model performance can de…
MetaFun: Meta-Learning with Iterative Functional Updates
Jin Xu, Jean-Francois Ton, Hyunjik Kim +2
We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a fin…