Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Geometry-aware 3D Generative Adversarial Networks   Unsupervised generation of high-quality multi-view-consistent images and 3D
shapes using only collections of single-view 2D photographs has been a
long-standing challenge. Existing 3D GANs are either compute-intensive or make
approximations that are not 3D-consistent; the former limits quality and
resolution of the generated images and the latter adversely affects multi-view
consistency and shape quality. In this work, we improve the computational
efficiency and image quality of 3D GANs without overly relying on these
approximations. We introduce an expressive hybrid explicit-implicit network
architecture that, together with other design choices, synthesizes not only
high-resolution multi-view-consistent images in real time but also produces
high-quality 3D geometry. By decoupling feature generation and neural
rendering, our framework is able to leverage state-of-the-art 2D CNN
generators, such as StyleGAN2, and inherit their efficiency and expressiveness.
We demonstrate state-of-the-art 3D-aware synthesis with FFHQ and AFHQ Cats,
among other experiments.

24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics