Name
Title | ||
|---|---|---|
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis |
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 radiance fields. The proposed approach obtains state-of-the-art results for 3D-aware image synthesis with multiple real and synthetic datasets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CVPR46437.2021.00574 | 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 |
DocType | ISSN | Citations |
Conference | 1063-6919 | 0 |
PageRank | References | Authors |
0.34 | 21 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric R. Chan | 1 | 1 | 1.06 |
| Marco Monteiro | 2 | 1 | 0.72 |
| Petr Kellnhofer | 3 | 122 | 10.45 |
| Yichen Wei | 4 | 814 | 47.77 |
| Gordon Wetzstein | 5 | 945 | 72.47 |