%0 Generic %1 chan2020pigan %A Chan, Eric R. %A Monteiro, Marco %A Kellnhofer, Petr %A Wu, Jiajun %A Wetzstein, Gordon %D 2020 %K GAN generation view_sythesis %T pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis %U http://arxiv.org/abs/2012.00926 %X 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.

@misc{chan2020pigan, 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.}, added-at = {2021-08-13T13:57:21.000+0200}, author = {Chan, Eric R. and Monteiro, Marco and Kellnhofer, Petr and Wu, Jiajun and Wetzstein, Gordon}, biburl = {https://www.bibsonomy.org/bibtex/2e2ed972d9ba484037168709dd479a6ed/shuncheng.wu}, description = {https://arxiv.org/pdf/2012.00926.pdf Project page: https://marcoamonteiro.github.io/pi-GAN-website/ Code: https://github.com/marcoamonteiro/pi-GAN}, interhash = {920abed8a15080074b1ba8c361d6ed07}, intrahash = {e2ed972d9ba484037168709dd479a6ed}, keywords = {GAN generation view_sythesis}, note = {cite arxiv:2012.00926}, timestamp = {2021-08-13T13:58:19.000+0200}, title = {pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis}, url = {http://arxiv.org/abs/2012.00926}, year = 2020 }