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A Space-Agnostic Visual Game Analytics Tool with Adaptive Spatial Reconstruction for Mixed Reality Game Development

Nahian Rifaat, Fedir Skliar, Parisa Sargolzaei, Loutfouz Zaman

arXiv:2610.08619v1cs.HC

TL;DR

MR game analytics lacks tools suited to complex physical-digital gameplay and location-independent session reconstruction. GAMR+ combines adaptive spatial reconstruction and object recognition for HoloLens 2, and its functionality was tested as a proof of concept on an in-house game.

  • Problem

    Existing MR game analytics tools do not support space-agnostic gameplay reconstruction, amid challenges handling complex physical-digital inputs.

    Source: §I · p. 1, §II · p. 1

  • Method

    GAMR+ combines adaptive spatial reconstruction and object recognition to reconstruct HoloLens 2 gameplay sessions independently of where they were recorded.

    Source: §III · p. 2, A. Adaptive Spatial Reconstruction · p. 2, §IV · p. 3

  • Results

    The tool's functionality was tested as a proof of concept on an in-house-developed game.

    Source: §V · p. 4

  • Takeaways & Limitations

    GAMR+ is intended to help game developers inspect MR game environments to detect and address bugs.

    Source: §V · p. 4

    Testing on a single in-house game limits generalization across different games and genres.

    Source: §IV · p. 4

Abstract

from arXiv · show

Recent years have seen an increasing need for MR tools for game development and visual game analytics. However, the number of visual game analytics tools for MR games remains scarce due to the unique complexities in integrating real components from both the physical and the digital worlds in MR games. Existing visual analytics tools for such games are not suitable for space-agnostic generalization. To tackle this problem, we introduce a space-agnostic visual game analytics tool that incorporates adaptive spatial reconstruction and real-time object detection built for the Microsoft HoloLens 2 MR headset. We believe that the outcomes of our work will increase insights into player environment and pave a new direction for visual game analytics for MR game development.

I. INTRODUCTION

MR games combine physical and digital elements in ways that complicate player interactions, while existing analytics tools lack location-independent session reconstruction. GAMR+ addresses this gap with a space-agnostic analytics tool and spatial reconstruction system.

I. INTRODUCTION

  • Integrating physical and digital elements creates distinctive MR gameplay complexity, including new complexity in player interactions.The passage frames this as a reason MR games need a different approach to game analytics.Source: §I · p. 1
  • Analyzing gameplay data within MR environments can give developers contextual information about game dynamics and player behaviour despite hardware limitations.The cited limitations include HoloLens 2's field of view and imperfect hand tracking.Source: §I · p. 1
  • MR movement, hand-tracking, visual, auditory, and haptic data can inform analysis of gameplay and physical and cognitive load.MR also supports intuitive exploration and manipulation of data in 3D.Source: §I · p. 1
  • Existing MR analytics tools lack location-independent gameplay reconstruction, leaving developers to review lengthy recordings manually in a specific location.The gap also includes managing complex, precise input data for visual analysis.Source: §I · p. 1
  • GAMR+ offers reconstructed gameplay sessions, data visualization, and object tracking as a space-agnostic analytics tool for MR games.The proposed spatial reconstruction system is independent of the gameplay session's location.Source: §I · p. 1

II. RELATED WORK

Prior MR visual analytics approaches do not provide space-agnostic spatial reconstruction for gameplay analysis.

II. RELATED WORK

  • Existing approaches typically rely on fixed environments, and none provides space-agnostic spatial reconstruction for MR visual analytics.The stated gap concerns reconstructing gameplay sessions independently of their original location.Source: §II · p. 1

A. Game Analytics

The cited game analytics work includes tools for modeling player navigation, reconstructing gameplay from recorded data, and analyzing MR gameplay through reconstructed sessions and multimodal tracking.

A. Game Analytics

  • PathOS models player perception, memory, and cognition to simulate visual and information processing along agent paths, with heatmaps and bubble visualizations.The simulated paths mimic player navigation.Source: A. Game Analytics · p. 2
  • MacCormick and Zaman analyze player behaviour by reconstructing gameplay from recorded data and replaying it from different perspectives.Source: A. Game Analytics · p. 2
  • Sargolzaei et al.'s MR analytics tool supports gameplay-issue identification through reconstructed sessions, heatmaps, annotations, and multimodal tracking.Tracked modalities include hands, camera, input, and audio.Source: A. Game Analytics · p. 2

B. Spatio-temporal Analytics

GAMR+ combines spatio-temporal visualization approaches with temporal data analysis while avoiding dependence on fixed gameplay environments.

B. Spatio-temporal Analytics

  • GAMR+ integrates spatio-temporal analysis, trajectories, hand-motion tracking, and colored player-trajectory trails without requiring fixed locations or environments.Kang and Kim's spatio-temporal analysis depended on fixed environments.Source: B. Spatio-temporal Analytics · p. 2

C. Temporal Analytics

  • Unlike TRUE's feedback from timestamped events and questionnaires, GAMR+ emphasizes space-agnostic reconstruction and real-time object recognition during playback.The object recognition enriches captured data with information about environmental objects.Source: C. Temporal Analytics · p. 2

D. Mixed Reality Analytics

Prior tools support XR visualization, spatial analytics, motion analysis, and MR user-study review, while GAMR+ focuses on in-situ visual analytics for MR games.

D. Mixed Reality Analytics

III. GAMR+

GAMR+ reconstructs HoloLens 2 gameplay sessions for analysis across locations, combining adaptive spatial reconstruction with object recognition and session visualization.

III. GAMR+

  • GAMR+ records gameplay sessions and reconstructs their environments elsewhere so developers can analyze player behavior and surroundings across locations.The pipeline scans the environment, records sessions, reconstructs the environment at another location, and supports visualization and comparison of sessions.Source: §III · p. 2
  • GAMR+ includes object recognition alongside recording and visualization in its gameplay-analysis pipeline.The UI and usage are shown in Fig. 2.Source: §III · p. 2, Figure 2 · p. 3

A. Adaptive Spatial Reconstruction

B. Real-Time Object Detection

GAMR+ runs object recognition on the HoloLens 2 camera feed, using a lightweight YOLOv8 model deployed on the headset.

B. Real-Time Object Detection

  • When GAMR+ launches, it performs real-time recognition on the HoloLens camera feed using Unity Barracuda and an ONNX-format lightweight YOLOv8 model.Source: B. Real-Time Object Detection · p. 3
  • The lightweight YOLOv8 model detects 80 object classes, including furniture, electronics, appliances, and kitchenware.Source: B. Real-Time Object Detection · p. 3
  • Integrating detections from the initial scan into spatial mesh data is proposed for future iterations to help analyze character interactions with environmental objects.The authors say this may help users detect interaction issues.Source: B. Real-Time Object Detection · p. 3

C. Action-Adventure game

The Action-Adventure game tests GAMR+ with a player-controlled character, enemy encounters, and a boss battle.

C. Action-Adventure game

IV. DISCUSSION

GAMR+ combines real-time object detection with adaptive spatial reconstruction to support location-independent analysis of gameplay sessions, including sessions recorded elsewhere. Its current validation and deployment are constrained by a single in-house game, HoloLens 2 hardware, privacy risks, and the absence of a developer user study.

IV. DISCUSSION

  • GAMR+ integrates real-time object detection with adaptive spatial reconstruction for location-independent gameplay-session analysis, including sessions recorded elsewhere.The tool is designed to complement video-footage review and automated bug detection.Source: §IV · pp. 3–4
  • Testing on a single in-house game limits generalization across games and genres, and the current iteration lacks a user study with MR game developers.Source: §IV · p. 4
  • HoloLens 2’s limited computational power delays and reduces the accuracy of real-time object detection.Source: §IV · p. 4
  • Spatial analytics in GAMR+ can pose ethical and privacy risks to users.Source: §IV · p. 4

V. CONCLUSION AND FUTURE WORK

The authors present GAMR+ as a work-in-progress tool for space-agnostic visual analytics in MR and test it as a proof of concept on an in-house game. Future work includes broader deployment options, cloud-based object detection, and evaluation with MR game developers.

V. CONCLUSION AND FUTURE WORK

  • GAMR+ is a work-in-progress space-agnostic visual analytics tool for MR, tested as a proof of concept on an in-house game.Source: §V · p. 4
  • Future deployment as a separate application could support any MR headset and games with spatial meshing, with game-engine plugins intended to make it cross-platform and game-agnostic.Source: §V · p. 4
  • The authors plan to move object detection models to cloud servers and evaluate the tool in a user study with MR game developers.Source: §V · p. 4
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