HI-GAN: Hierarchical Inpainting GAN with Auxiliary Inputs for Combined RGB and Depth Inpainting

TL;DR

HI-GAN introduces a hierarchical GAN framework utilizing auxiliary edge and label inputs to improve RGB-D inpainting, especially for mixed reality applications, achieving superior results over existing methods.

Contribution

This work is the first to incorporate label images into an end-to-end hierarchical inpainting GAN for RGB-D data, enhancing inpainting quality with auxiliary inputs.

Findings

Outperforms existing RGB-D inpainting methods in quality and consistency.

Effectively utilizes auxiliary edge and label images for improved inpainting.

Operates in an end-to-end training framework with hierarchical optimization.

Abstract

Inpainting involves filling in missing pixels or areas in an image, a crucial technique employed in Mixed Reality environments for various applications, particularly in Diminished Reality (DR) where content is removed from a user's visual environment. Existing methods rely on digital replacement techniques which necessitate multiple cameras and incur high costs. AR devices and smartphones use ToF depth sensors to capture scene depth maps aligned with RGB images. Despite speed and affordability, ToF cameras create imperfect depth maps with missing pixels. To address the above challenges, we propose Hierarchical Inpainting GAN (HI-GAN), a novel approach comprising three GANs in a hierarchical fashion for RGBD inpainting. EdgeGAN and LabelGAN inpaint masked edge and segmentation label images respectively, while CombinedRGBD-GAN combines their latent representation outputs and performs RGB and Depth inpainting. Edge images and particularly segmentation label images as auxiliary inputs significantly enhance inpainting performance by complementary context and hierarchical optimization. We believe we make the first attempt to incorporate label images into inpainting process.Unlike previous approaches requiring multiple sequential models and separate outputs, our work operates in an end-to-end manner, training all three models simultaneously and hierarchically. Specifically, EdgeGAN and LabelGAN are first optimized separately and further optimized inside CombinedRGBD-GAN to enhance inpainting quality. Experiments demonstrate that HI-GAN works seamlessly and achieves overall superior performance compared with existing approaches.