pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
Eric R. Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, Gordon Wetzstein
4/5/2021
Keywords: Generalization, Generative Models, Global Conditioning
Venue: CVPR 2021
Bibtex:
@inproceedings{chan2021pigan,
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
author = {Eric R. Chan and Marco Monteiro and Petr Kellnhofer and Jiajun Wu and Gordon Wetzstein},
title = {pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis},
year = {2021},
url = {http://arxiv.org/abs/2012.00926v2},
entrytype = {inproceedings},
id = {chan2021pigan}
}
Abstract
We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering. Existing approaches however fall short in two ways: first, they may lack an underlying 3D representation or rely on view-inconsistent rendering, hence synthesizing images that are not multi-view consistent; second, they often depend upon representation network architectures that are not expressive enough, and their results thus lack in image quality. We propose a novel generative model, named Periodic Implicit Generative Adversarial Networks ($\pi$-GAN or pi-GAN), for high-quality 3D-aware image synthesis. $\pi$-GAN leverages neural representations with periodic activation functions and volumetric rendering to represent scenes as view-consistent 3D representations with fine detail. The proposed approach obtains state-of-the-art results for 3D-aware image synthesis with multiple real and synthetic datasets.
Citation Graph
(Double click on nodes to open corresponding papers' pages)
(Double click on nodes to open corresponding papers' pages)
* Showing citation graph for papers within our database. Data retrieved from Semantic Scholar. For full citation graphs, visit ConnectedPapers.