Background Augmentation Generative Adversarial Networks (BAGANs): Effective Data Generation Based on GAN-Augmented 3D Synthesizing

Abstract

Augmented Reality (AR) is crucial for immersive Human⁻Computer Interaction (HCI) and the vision of Artificial Intelligence (AI). Labeled data drives object recognition in AR. However, manually annotating data is expensive, labor-intensive, and data distribution asymmetry. Scantily labeled data limits the application of AR. Aiming at solving the problem of insufficient and asymmetry training data in AR object recognition, an automated vision data synthesis method, i.e., background augmentation generative adversarial networks (BAGANs), is proposed in this paper based on 3D modeling and the Generative Adversarial Network (GAN) algorithm. Our approach has been validated to have better performance than other methods through image recognition tasks with respect to the natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand its application scope, which is of great significance for immersive interactive systems.

Keywords

• object recognition
• image data synthesizing
• human–computer interaction
• data synthesizing for immersive HCI
• generative adversarial nets
• background augmentation generative adversarial networks (BAGANs)