Application of OpenPose-Based Skeletal Binding in Generative AI: Technical Issues And Regulatory Concerns

Abstract
In recent years, generative artificial intelligence (AI) has rapidly developed and shown a growing trend of integration with other technologies, particularly those related to human pose recognition. For example, by incorporating OpenPose skeletal diagrams into the ControlNet architecture for models such as Stable Diffusion, it is possible to precisely adjust character postures in generated images. This study introduces the foundations of generative AI and applies methods such as literature analysis, case studies, and semi-structured expert interviews. Experts pointed out that generative AI raises issues of copyright ownership and personal privacy. The training data often comes from online resources, creating disputes over the rights of original authors. Meanwhile, skeletal data represents sensitive biometric information, and its leakage can lead to privacy and security risks. Therefore, this paper proposes comprehensive strategies including technical optimization, regulatory prudence, and industry oversight to guide the standardized development of this field.

Keywords
OpenPose,Skeletal Binding,Pose Control,Regulatory Risks

Cite this paper
ZiYuan Xue, ShiYu Chen, Application of OpenPose-Based Skeletal Binding in Generative AI: Technical Issues And Regulatory Concerns , SCIREA Journal of Sociology. Volume 9, Issue 5, October 2025 | PP. 331-347. 10.54647/sociology841482

References

[ 1 ]	Guo Xinying. Legal Regulation of Generative AI Services under Digital Ethics[J]. Open Journal of Legal Science, 2023, 11(06):4979–4986. https://doi.org/10.12677/OJLS.2023.116712
[ 2 ]	Konrad SG, Masson FR. Pedestrian Skeleton Tracking Using OpenPose and Probabilistic Filtering[C]. 2020 IEEE Congreso Bienal de Argentina (ARGENCON), 2020:1–7.
[ 3 ]	Cao Z, Hidalgo G, Simon T, Wei S-E, Sheikh Y. OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields[J]. arXiv preprint arXiv:1812.08008, 2019.
[ 4 ]	Zhang L, Rao A, Agrawala M. Adding Conditional Control to Text-to-Image Diffusion Models[C]. 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023:3813–3824.
[ 5 ]	Li B, Williamson J, Kelp N, Dick T, Bo APL. Towards balance assessment using OpenPose[C]. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021:7605–7608.
[ 6 ]	Otsuka K, Yagi N, Yamanaka Y, Hata Y, Sakai Y. Joint Position Registration between OpenPose and Motion Analysis for Rehabilitation[C]. 2020 IEEE 50th International Symposium on Multiple-Valued Logic (ISMVL), 2020:100–104.Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative Adversarial Networks[J]. arXiv preprint arXiv:1406.2661, 2014.
[ 7 ]	Liu H, Mei J, Jia F, Zheng D, Yuan J, Wang Z, Zhao Y. AuraPose: Accurate Human Pose Detection and Behavior Recognition via Enhanced OpenPose with Angular Measurement[C]. 2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2024:1–6.
[ 8 ]	Mei Jian. Research and Implementation of a Ski Training Posture Correction System Based on OpenPose[D]. Jilin Institute of Chemical Technology, 2024.
[ 9 ]	Chen W, Jiang Z, Guo H, Ni X. Fall Detection Based on Key Points of Human-Skeleton Using OpenPose[J]. Symmetry, 2020, 12(5):744.
[ 10 ]	Jo B, Kim S. Comparative Analysis of OpenPose, PoseNet, and MoveNet Models for Pose Estimation in Mobile Devices[J]. Traitement Du Signal, 2022, 39(1):119–124.
[ 11 ]	Bao Saijun, Tang Sihui. Study on Fair Use of Works in Training Generative AI Models[J].