SideGAN: 3D-Aware Generative Model for Improved Side-View Image Synthesis

TL;DR

SideGAN introduces a dual-branch discriminator and pose-matching loss to improve 3D-aware image synthesis, especially for side-view faces, by addressing pose imbalance and enhancing pose consistency in generative models.

Contribution

The paper proposes a novel 3D GAN training method with a dual-branched discriminator and pose-matching loss to improve image quality across all camera angles.

Findings

Enhanced image quality at side-view angles

Improved pose consistency in generated images

Effective handling of pose-imbalanced datasets

Abstract

While recent 3D-aware generative models have shown photo-realistic image synthesis with multi-view consistency, the synthesized image quality degrades depending on the camera pose (e.g., a face with a blurry and noisy boundary at a side viewpoint). Such degradation is mainly caused by the difficulty of learning both pose consistency and photo-realism simultaneously from a dataset with heavily imbalanced poses. In this paper, we propose SideGAN, a novel 3D GAN training method to generate photo-realistic images irrespective of the camera pose, especially for faces of side-view angles. To ease the challenging problem of learning photo-realistic and pose-consistent image synthesis, we split the problem into two subproblems, each of which can be solved more easily. Specifically, we formulate the problem as a combination of two simple discrimination problems, one of which learns to discriminate whether a synthesized image looks real or not, and the other learns to discriminate whether a synthesized image agrees with the camera pose. Based on this, we propose a dual-branched discriminator with two discrimination branches. We also propose a pose-matching loss to learn the pose consistency of 3D GANs. In addition, we present a pose sampling strategy to increase learning opportunities for steep angles in a pose-imbalanced dataset. With extensive validation, we demonstrate that our approach enables 3D GANs to generate high-quality geometries and photo-realistic images irrespective of the camera pose.