Shared Attention across Mixed-Reality Configurations: AR-AR and AR-VR Dyads

Master Thesis open

Project overview

Our recent VR dyad study established the kinematic cue hierarchy for shared attention in the symmetric VR-VR case: both partners see fully virtual avatars under identical rendering. Mixed-reality collaboration breaks that symmetry. In AR-AR, partners see each other’s real bodies but with faces partly hidden behind headsets; in AR-VR, one partner sees an avatar while the other sees a real person composited into a virtual scene. Each configuration distorts different channels, so the cue hierarchy may shift per configuration [1, 2].

Project motivation

Cross-device collaboration is the realistic deployment scenario: field studies of MR telepresence consistently mix headsets and modalities [1, 3]. If the torso-first hierarchy survives every configuration, systems can standardize on body-pose sharing as the attention backbone; if AR’s visible-but-headset-occluded faces change the picture, designers need configuration-specific cue augmentation (for example rendered gaze rays when the eyes are hidden [3]). The existing task, targets, and analysis pipeline transfer directly, making a clean three-configuration comparison feasible within one thesis.

Project goals

  1. Configuration implementation. Extend the existing setup to AR-AR and AR-VR variants (video passthrough or optical see-through headsets in the same tracked room, reusing the OptiTrack pipeline for body features).
  2. Hierarchy comparison. Run dyads through the decoding task per configuration and compare the eye/head/body cue hierarchies and overall accuracy against the VR-VR baseline with the existing variance-decomposition tooling.

You will

  • Perform a literature review on gaze and attention cues in MR remote collaboration
  • Implement AR-AR and AR-VR variants of the existing Unity environment
  • Run a dyad study across configurations with the existing pipeline
  • Compare cue hierarchies across configurations
  • Summarize your findings in a thesis and present them to an audience
  • (Optional) co-write a research paper

You need

  • Strong communication skills in English
  • Good knowledge of Unity (XR toolchains; passthrough experience a plus)
  • Comfort with hardware setup and calibration

References

  1. Piumsomboon, T., Lee, G. A., Hart, J. D., Ens, B., Lindeman, R. W., Thomas, B. H., & Billinghurst, M. (2018). Mini-Me: An adaptive avatar for mixed reality remote collaboration. CHI 2018.
  2. Jing, A., May, K., Lee, G., & Billinghurst, M. (2022). The impact of sharing gaze behaviours in collaborative mixed reality. PACM HCI (CSCW).
  3. Bai, H., Sasikumar, P., Yang, J., & Billinghurst, M. (2020). A user study on mixed reality remote collaboration with eye gaze and hand gesture sharing. CHI 2020.
  4. The parent study on avatar-mediated shared attention in virtual reality; details are available from the advisor on request.

Keywords: Mixed Reality, AR, Remote Collaboration, Shared Attention, Avatars

Interested in this topic? Reach out through your university student email address via the contact form, with a short motivation, your transcript of records and, if available, a CV.

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