pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis

Eric R. Chan; Marco Monteiro; Petr Kellnhofer; Jiajun Wu; Gordon Wetzstein

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arXiv · 2012.00926

pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis

Eric R. Chan·Marco Monteiro·Petr Kellnhofer·Jiajun Wu·Gordon Wetzstein

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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.

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Eric R. Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, Gordon Wetzstein. 2020-12-02. pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis. https://arxiv.org/abs/2012.00926

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