PanoTree: Autonomous Photo-Spot Explorer in Virtual Reality Scenes

TL;DR

PanoTree is an automated system that identifies and explores aesthetically appealing photo spots in virtual reality scenes, enhancing user experience and supporting VR content creation through deep learning and optimization algorithms.

Contribution

The paper introduces PanoTree, combining a deep scoring network and a Hierarchical Optimistic Optimization algorithm for efficient photo-spot exploration in VR environments, which is a novel approach.

Findings

Scoring network achieves human-level photo aesthetic assessment.

Efficient exploration of VR scenes with the proposed HOO algorithm.

Applications include automatic thumbnail generation and visitor flow planning.

Abstract

Social VR platforms enable social, economic, and creative activities by allowing users to create and share their own virtual spaces. In social VR, photography within a VR scene is an important indicator of visitors' activities. Although automatic identification of photo spots within a VR scene can facilitate the process of creating a VR scene and enhance the visitor experience, there are challenges in quantitatively evaluating photos taken in the VR scene and efficiently exploring the large VR scene. We propose PanoTree, an automated photo-spot explorer in VR scenes. To assess the aesthetics of images captured in VR scenes, a deep scoring network is trained on a large dataset of photos collected by a social VR platform to determine whether humans are likely to take similar photos. Furthermore, we propose a Hierarchical Optimistic Optimization (HOO)-based search algorithm to efficiently explore 3D VR spaces with the reward from the scoring network. Our user study shows that the scoring network achieves human-level performance in distinguishing randomly taken images from those taken by humans. In addition, we show applications using the explored photo spots, such as automatic thumbnail generation, support for VR world creation, and visitor flow planning within a VR scene.