Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach

D. Tome, C. Russell, and L. Agapito, “Lifting from the deep: Convolutional 3d pose estimation from a single image,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017

2017

Earlier work this paper cites.

C. L. C. and M. A. Marini, Contest theory . Edward Elgar Publishing, 2018, vol. II, p. 221–221

2018

Earlier work this paper cites.

D. Pavllo, C. Feichtenhofer, D. Grangier, and M. Auli, “3d human pose estimation in video with temporal convolutions and semi-supervised training,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Los Alamitos, CA, USA: IEEE Computer Society, jun 2019, pp. 7745–7754. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/CVPR.2019.00794

2019

Earlier work this paper cites.

J. Lu, S. Yang, Z. Zhang, J. Han, H. Peng, z. Hu, and T. Wang, “Multi-level two-sided rating protocol design for service exchange contest dilemma in crowdsensing,” IEEE Access , vol. 7, pp. 78 391–78 405, 2019

2019

Earlier work this paper cites.

M. Contributors, “Openmmlab pose estimation toolbox and benchmark,” https://github.com/open-mmlab/mmpose , 2020

2020

Earlier work this paper cites.

Y. Ren, X. Chen, S. Guo, S. Guo, and A. Xiong, “Blockchain-based vec network trust management: A drl algorithm for vehicular service offloading and migration,” IEEE Transactions on Vehicular Technology , vol. 70, no. 8, pp. 8148–8160, 2021

2021

Cited alongside, same era.

F. Tang, X. Chen, M. Zhao, and N. Kato, “The roadmap of communication and networking in 6g for the metaverse,” IEEE Wireless Communications , pp. 1–15, 2022

2022

Cited alongside, same era.

M. Kono, N. Morimoto, and R. Kaku, “Vcposer: Interactive pose generation of virtual characters corresponding to human pose input,” in Proceedings of the 28th ACM Symposium on Virtual Reality Software and Technology , ser. VRST ’22. New York, NY, USA: Association for Computing Machinery, 2022. [Online]. Available: https://doi.org/10.1145/3562939.3565640

2022

Cited alongside, same era.

J. Yang, Y. Zhou, H. Huang, H. Zou, and L. Xie, “MetaFi: Device-free pose estimation via commodity WiFi for metaverse avatar simulation,” arXiv preprint arXiv:2208.10414 , 2022

Original

Y. Han, D. Niyato, C. Leung, C. Miao, and D. I. Kim, “A dynamic resource allocation framework for synchronizing metaverse with iot service and data,” in ICC 2022 - IEEE International Conference on Communications , May 2022, p. 1196–1201

2022

Later among the works it cites.

H. Chen, R. Feng, S. Wu, H. Xu, F. Zhou, and Z. Liu, “2d human pose estimation: A survey,” 2022

2022

Later among the works it cites.

H. Du, J. Liu, D. Niyato, J. Kang, Z. Xiong, J. Zhang, and D. I. Kim, “Attention-aware resource allocation and qoe analysis for metaverse xurllc services,” no. arXiv:2208.05438, Feb 2023, arXiv:2208.05438 [cs]. [Online]. Available: http://arxiv.org/abs/2208.05438

Original

2023

Closest in time.

J. Wang, H. Du, Z. Tian, D. Niyato, J. Kang, and X. Shen, “Semantic-aware sensing information transmission for metaverse: A contest theoretic approach,” IEEE Transactions on Wireless Communications , pp. 1–1, 2023

2023

Closest in time.