References
[1]. W. Xian, J.-B. Huang, J. Kopf, and C. Kim, “Space-time neural irradiance fields for free-viewpoint video,” 2020.
[2]. M. Xu et al., “A full dive into realizing the edge-enabled metaverse: Visions, enabling technologies, and challenges,” IEEE Communications surveys and tutorials, vol. 25, Art. no. 1, 2023.
[3]. A. Puder, S. Markwitz, F. Gudermann, and K. Geihs, “AI-based trading in open distributed environments,” K. Raymond and L. Armstrong, Eds. Springer US, 1995, pp. 157–169. doi: https://doi.org/10.1007/978-0-387-34882-7%E2%82%812.
[4]. R. Jiang, P. Chazot, N. Pavese, D. Crookes, A. Bouridane, and C. M. Emre, “Private facial prediagnosis as an edge service for parkinson’s DBS treatment valuation,” IEEE journal of biomedical and health informatics, vol. 26, Art. no. 6, 2022.
[5]. A. Alharthi, Q. Ni, and R. Jiang, “A privacy-preservation framework based on biometrics blockchain (BBC) to prevent attacks in VANET,” IEEE access, vol. 9, pp. 87299–87309, 2021.
[6]. R. Jiang, M. L. Parry, P. A. Legg, C. David, and I. W. Griffiths, “Automated 3-D animation from snooker videos with information-theoretical optimization,” IEEE transactions on computational intelligence and AI in games., vol. 5, Art. no. 4, 2013.
[7]. Y. R. V, B. Klare, and J. A. K, “Face recognition in the virtual world: Recognizing avatar faces,” 2012, vol. 1, pp. 40–45.
[8]. S. Liu, Ngiam, Kee Yuan, and M. Feng, “Deep reinforcement learning for clinical decision support: A brief survey,” 2019.
[9]. Ando and Thawonmas, “LEVEL OF INTEREST IN OBSERVED EXHIBITS IN METAVERSE MUSEUMS,” 2013.
[10]. J.-L. Lugrin and M. Cavazza, “AI-based world behaviour for emergent narratives,” 2006, pp. 25–es.
[11]. K. B. Ravi et al., “Deep reinforcement learning for autonomous driving: A survey,” IEEE transactions on intelligent transportation systems, vol. 23, Art. no. 6, 2022.
[12]. X. Liao et al., “Iteratively-refined interactive 3D medical image segmentation with multi-agent reinforcement learning,” 2019.
[13]. OpenAI et al., “Dota 2 with large scale deep reinforcement learning,” 2019.
Cite this article
Ren,K. (2023). Research and application of deep learning in Metaverse. Applied and Computational Engineering,14,265-273.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. W. Xian, J.-B. Huang, J. Kopf, and C. Kim, “Space-time neural irradiance fields for free-viewpoint video,” 2020.
[2]. M. Xu et al., “A full dive into realizing the edge-enabled metaverse: Visions, enabling technologies, and challenges,” IEEE Communications surveys and tutorials, vol. 25, Art. no. 1, 2023.
[3]. A. Puder, S. Markwitz, F. Gudermann, and K. Geihs, “AI-based trading in open distributed environments,” K. Raymond and L. Armstrong, Eds. Springer US, 1995, pp. 157–169. doi: https://doi.org/10.1007/978-0-387-34882-7%E2%82%812.
[4]. R. Jiang, P. Chazot, N. Pavese, D. Crookes, A. Bouridane, and C. M. Emre, “Private facial prediagnosis as an edge service for parkinson’s DBS treatment valuation,” IEEE journal of biomedical and health informatics, vol. 26, Art. no. 6, 2022.
[5]. A. Alharthi, Q. Ni, and R. Jiang, “A privacy-preservation framework based on biometrics blockchain (BBC) to prevent attacks in VANET,” IEEE access, vol. 9, pp. 87299–87309, 2021.
[6]. R. Jiang, M. L. Parry, P. A. Legg, C. David, and I. W. Griffiths, “Automated 3-D animation from snooker videos with information-theoretical optimization,” IEEE transactions on computational intelligence and AI in games., vol. 5, Art. no. 4, 2013.
[7]. Y. R. V, B. Klare, and J. A. K, “Face recognition in the virtual world: Recognizing avatar faces,” 2012, vol. 1, pp. 40–45.
[8]. S. Liu, Ngiam, Kee Yuan, and M. Feng, “Deep reinforcement learning for clinical decision support: A brief survey,” 2019.
[9]. Ando and Thawonmas, “LEVEL OF INTEREST IN OBSERVED EXHIBITS IN METAVERSE MUSEUMS,” 2013.
[10]. J.-L. Lugrin and M. Cavazza, “AI-based world behaviour for emergent narratives,” 2006, pp. 25–es.
[11]. K. B. Ravi et al., “Deep reinforcement learning for autonomous driving: A survey,” IEEE transactions on intelligent transportation systems, vol. 23, Art. no. 6, 2022.
[12]. X. Liao et al., “Iteratively-refined interactive 3D medical image segmentation with multi-agent reinforcement learning,” 2019.
[13]. OpenAI et al., “Dota 2 with large scale deep reinforcement learning,” 2019.