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[bibtex]@InProceedings{Chan_2021_CVPR, author = {Chan, Eric R. and Monteiro, Marco and Kellnhofer, Petr and Wu, Jiajun and Wetzstein, Gordon}, title = {Pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {5799-5809} }
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 (p-GAN or pi-GAN), for high-quality 3D-aware image synthesis. p-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.
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