Recent advances in 3D reconstruction technology allow people to capture and share their experiences in 3D. However, little is known about people’s sharing preferences and privacy concerns for these reconstructed experiences. To fill this gap, we first present ReliveReality, an experience-sharing method utilizing deep learning-based computer vision techniques to reconstruct clothed humans and 3D environments and estimate 3D pose with only a RGB camera. ReliveReality can be integrated into social virtual environments, allowing others to socially relive a shared experience by moving around the experience from different perspectives, on desktop or in VR. We conducted a 44-participant within-subject study to compare ReliveReality to viewing recorded videos, and to a ReliveReality version with blurring obfuscation. Our results shed light on how people identify with reconstructed avatars, how obfuscation affects reliving experiences and sharing preferences and privacy concerns for reconstructed experiences. We propose design implications for addressing these issues.
https://doi.org/10.1145/3476078
The 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing