Crowd-Density Runtime Performance Analysis and NPC Optimisation in a Standalone Virtual Reality Tawaf Simulation

Standalone virtual reality (VR) can support portable Tawaf simulation, but its mobile-class resources limit the number of non-player characters (NPCs) that can be moved, animated, and rendered while stable frame delivery is maintained. This paper analyses crowd-density runtime performance, evaluates selected NPC update-rate and animation optimisations, and determines supported density on a PICO 4 Ultra. A quantitative device-based benchmark used six crowd levels (0, 25, 50, 75, 100, and 150 NPCs), seven baseline and optimisation conditions, and three repeated runs per combination, producing 126 runs and 44,430 clean samples. Performance was evaluated using mean frames per second (FPS), mean and percentile frame time, late-frame percentage, memory behaviour, and a predefined practical near-90 Hz rule requiring at least 89 FPS and no more than 11.20 ms mean frame time. The baseline supported 25 NPCs. Updating NPCs every three frames increased support to 75 NPCs, animation simplification supported 100 NPCs, and distance-based animation, animation culling, and combined optimisation supported the highest tested density of 150 NPCs. At 150 NPCs, combined optimisation increased mean FPS by 24.00% and reduced mean frame time by 19.37% relative to baseline. Animation culling was marginally stronger on several high-density indicators, demonstrating that optimisation effects were not fully additive. Strict 90 Hz mean and P95 stability were not achieved, and invalid CPU/GPU timing fields were excluded from processor-level attribution. The study contributes a controlled standalone-headset benchmarking framework and an evidence-based mapping between NPC optimisation configurations and supported Tawaf crowd density. These results provide practical guidance for configuring animated crowds in resource-constrained standalone VR applications intended for ritual familiarisation, training, and related immersive experiences.

Crowd Density, NPC Optimisation, Runtime Performance, Standalone Virtual Reality, Tawaf Simulation

1. Abidin, A. R. Z., Masmuzidin, M. Z., Hussien, N. S., & Mohaidin, M. (2022). Virtual Hajj: The development of virtual environment for learning about Hajj and Umrah. AIP Conference Proceedings, 040005. https://doi.org/10.1063/5.0119708 [Google Scholar] [Crossref]

2. Beacco, A., Pelechano, N., & Andújar, C. (2016). A survey of real-time crowd rendering. Computer Graphics Forum, 35(8), 32–50. https://doi.org/10.1111/cgf.12774 [Google Scholar] [Crossref]

3. Curtis, S., Guy, S. J., Zafar, B., & Manocha, D. (2011). Virtual Tawaf: A case study in simulating the behavior of dense, heterogeneous crowds. 2011 IEEE International Conference on Computer Vision Workshops, 128–135. https://doi.org/10.1109/ICCVW.2011.6130234 [Google Scholar] [Crossref]

4. Dang, H.-T., Gaudou, B., & Verstaevel, N. (2024). A literature review of dense crowd simulation. Simulation Modelling Practice and Theory, 134, 102955. https://doi.org/10.1016/j.simpat.2024.102955 [Google Scholar] [Crossref]

5. Dufour, O., Dang, H.-T., Cordes, J., Korbmacher, R., Chraibi, M., Gaudou, B., Nicolas, A., & Tordeux, A. (2025). Dense crowd dynamics and pedestrian trajectories: A multiscale field dataset from the festival of lights in Lyon. Scientific Data, 12(1), 718. https://doi.org/10.1038/s41597-025-04732-3 [Google Scholar] [Crossref]

6. Geris, A., Cukurbasi, B., Kilinc, M., & Teke, O. (2024). Balancing performance and comfort in virtual reality: A study of FPS, latency, and batch values. Software: Practice and Experience, 54(12), 2336–2348. https://doi.org/10.1002/spe.3356 [Google Scholar] [Crossref]

7. Gomez-Nogales, G., Prieto-Martin, M., Romero, C., Comino-Trinidad, M., Ramon-Prieto, P., Olivier, A.-H., Hoyet, L., Otaduy, M. A., Pettré, J., & Casas, D. (2024). Resolving collisions in dense 3D crowd animations. ACM Transactions on Graphics, 43(5), 163:1–163:14. https://doi.org/10.1145/3687266 [Google Scholar] [Crossref]

8. Grande, R., Albusac, J., Vallejo, D., Glez-Morcillo, C., & Castro-Schez, J. J. (2024). Performance evaluation and optimization of 3D models from low-cost 3D scanning technologies for virtual reality and metaverse e-commerce. Applied Sciences, 14(14), 6037. https://doi.org/10.3390/app14146037 [Google Scholar] [Crossref]

9. Hamilton, D., McKechnie, J., Edgerton, E., & Wilson, C. (2021). Immersive virtual reality as a pedagogical tool in education: A systematic literature review of quantitative learning outcomes and experimental design. Journal of Computers in Education, 8(1), 1–32. https://doi.org/10.1007/s40692-020-00169-2 [Google Scholar] [Crossref]

10. Hu, M., Luo, X., Chen, J., Lee, Y. C., Zhou, Y., & Wu, D. (2021). Virtual reality: A survey of enabling technologies and its applications in IoT. Journal of Network and Computer Applications, 178, 102970. https://doi.org/10.1016/j.jnca.2020.102970 [Google Scholar] [Crossref]

11. Jongbloed, J., Chaker, R., & Lavoué, E. (2024). Immersive procedural training in virtual reality: A systematic literature review. Computers & Education, 221, 105124. https://doi.org/10.1016/j.compedu.2024.105124 [Google Scholar] [Crossref]

12. Khan, S., & Deng, Z. (2024). Agent-based crowd simulation: An in-depth survey of determining factors for heterogeneous behavior. The Visual Computer, 40(7), 4993–5004. https://doi.org/10.1007/s00371-024-03503-2 [Google Scholar] [Crossref]

13. Koulaxidis, G., & Xinogalos, S. (2022). Improving mobile game performance with basic optimization techniques in Unity. Modelling, 3(2), 201–223. https://doi.org/10.3390/modelling3020014 [Google Scholar] [Crossref]

14. Makinoshima, F., & Oishi, Y. (2022). Crowd flow forecasting via agent-based simulations with sequential latent parameter estimation from aggregate observation. Scientific Reports, 12(1), 11168. https://doi.org/10.1038/s41598-022-14646-4 [Google Scholar] [Crossref]

15. Makransky, G., & Petersen, G. B. (2021). The cognitive affective model of immersive learning (CAMIL): A theoretical research-based model of learning in immersive virtual reality. Educational Psychology Review, 33(3), 937–958. https://doi.org/10.1007/s10648-020-09586-2 [Google Scholar] [Crossref]

16. Nusrat, F., Hassan, F., Zhong, H., & Wang, X. (2021). How developers optimize virtual reality applications: A study of optimization commits in open source Unity projects. 43rd IEEE/ACM International Conference on Software Engineering (ICSE), 473–485. https://doi.org/10.1109/ICSE43902.2021.00052 [Google Scholar] [Crossref]

17. Owaidah, A. A., Olaru, D., Bennamoun, M., Sohel, F., & Khan, R. N. (2021). Modelling mass crowd using discrete event simulation: A case study of integrated Tawaf and Sayee rituals during Hajj. IEEE Access, 9, 79424–79448. https://doi.org/10.1109/ACCESS.2021.3083265 [Google Scholar] [Crossref]

18. Paduraru, C., & Paduraru, M. (2022). Techniques for skeletal-based animation in massive crowd simulations. Computers, 11(2), 21. https://doi.org/10.3390/computers11020021 [Google Scholar] [Crossref]

19. Papadopoulos, A. V., Versluis, L., Bauer, A., Herbst, N., von Kistowski, J., Ali-Eldin, A., Abad, C. L., Amaral, J. N., Tuma, P., & Iosup, A. (2021). Methodological principles for reproducible performance evaluation in cloud computing. IEEE Transactions on Software Engineering, 47(8), 1528–1543. https://doi.org/10.1109/TSE.2019.2927908 [Google Scholar] [Crossref]

20. Radhakrishnan, U., Koumaditis, K., & Chinello, F. (2021). A systematic review of immersive virtual reality for industrial skills training. Behaviour & Information Technology, 40(12), 1310–1339. https://doi.org/10.1080/0144929X.2021.1954693 [Google Scholar] [Crossref]

21. Rendevski, N., Veljanovski, K., & Emini, N. (2023). FPS performance factors in standalone virtual reality applications. 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), 349–352. https://doi.org/10.1109/ICEST58410.2023.10187389 [Google Scholar] [Crossref]

22. Sadek Hosny, Y., Salem, M. A.-M., & Wahby, A. (2020). Performance optimization for standalone virtual reality headsets. 2020 IEEE Graphics and Multimedia (GAME), 13–18. https://doi.org/10.1109/GAME50158.2020.9315059 [Google Scholar] [Crossref]

23. Stancu, F.-V., Weiss, T., & Kuffner dos Anjos, R. (2025). Foveated animations for efficient crowd simulation. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 8(1), 1–14. https://doi.org/10.1145/3728306 [Google Scholar] [Crossref]

24. Sukaridhoto, S., Haz, A. L., Fajrianti, E. D., & Budiarti, R. P. N. (2023). Comparative study of 3D assets optimization of virtual reality application on VR standalone device. International Journal on Advanced Science, Engineering and Information Technology, 13(3), 999–1008. https://doi.org/10.18517/ijaseit.13.3.18375 [Google Scholar] [Crossref]

25. Toledo, L., De Gyves, O., & Rudomín, I. (2014). Hierarchical level of detail for varied animated crowds. The Visual Computer, 30(6–8), 949–961. https://doi.org/10.1007/s00371-014-0975-9 [Google Scholar] [Crossref]

26. Tusher, H. M., Mallam, S., & Nazir, S. (2024). A systematic review of virtual reality features for skill training. Technology, Knowledge and Learning, 29(2), 843–878. https://doi.org/10.1007/s10758-023-09713-2 [Google Scholar] [Crossref]

27. Wang, J., Shi, R., Xiao, Z., Qin, X., & Liang, H.-N. (2022). Effect of render resolution on gameplay experience, performance, and simulator sickness in virtual reality games. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 5(1), 1–15. https://doi.org/10.1145/3522610 [Google Scholar] [Crossref]

28. Wang, J., Shi, R., Zheng, W., Xie, W., Kao, D., & Liang, H.-N. (2023). Effect of frame rate on user experience, performance, and simulator sickness in virtual reality. IEEE Transactions on Visualization and Computer Graphics, 29(5), 2478–2488. https://doi.org/10.1109/TVCG.2023.3247057 [Google Scholar] [Crossref]

29. Wang, L., Shi, X., & Liu, Y. (2023). Foveated rendering: A state-of-the-art survey. Computational Visual Media, 9(2), 195–228. https://doi.org/10.1007/s41095-022-0306-4 [Google Scholar] [Crossref]

30. Weiss, T., Litteneker, A., Jiang, C., & Terzopoulos, D. (2019). Position-based real-time simulation of large crowds. Computers & Graphics, 78, 12–22. https://doi.org/10.1016/j.cag.2018.10.008 [Google Scholar] [Crossref]

• LeafQuest: A Mobile-Based Augmented Reality for Plant Placement, Discovery, and Growth
• Participatory Ergonomic Intervention Approach on Musculoskeletal Disorder (MSD) in Construction Sectors: A Systematic Review
• Integrating GIS into Traffic Incident Management: A Web-Based System
• RideSmart: A Personalized Motorcycle Product Recommendation System Using TF-IDF and Descriptive Analytics for Javidson Motorshop
• Educational Technology Course Design in Pre-Service Teachers Education: A Bibliometric Review of the Research Landscape