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JBE, vol. 27, no. 5, pp.751-761, September, 2022
3D Volumetric Capture-based Dynamic Face Production for Hyper-Realistic Metahuman
Moon-Seok Oh, Gyu-Hoon Han, and Young-Ho Seo
C.A E-mail: hyper-realistic, metahuman, volumetric, face model, 3d scanning, metaverse
With the development of digital graphics technology, the metaverse has become a significant trend in the content market. The demand for technology that generates high-quality 3D (dimension) models is rapidly increasing. Accordingly, various technical attempts are being made to create high-quality 3D virtual humans represented by digital humans. 3D volumetric capture is spotlighted as a technology that can create a 3D manikin faster and more precisely than the existing 3D model creation method. In this study, we try to analyze 3D high-precision facial production technology based on practical cases of the difficulties in content production and technologies applied in volumetric 3D and 4D model creation. Based on the actual model implementation case through 3D volumetric capture, we considered techniques for 3D virtual human face production and producted a new metahuman using a graphics pipeline for an efficient human facial generation.
Keyword: hyper-realistic, metahuman, volumetric, face model, 3d scanning, metaverse
 Lee, S.-L, “The Meanings of Fashion on the Social Media of Virtual Influencer Lil Miquela,” Journal of Digital Convergence, 19(9), pp. 323–333, 2021. doi: https://doi.org/10.14400/JDC.2021.19.9.323
 S. Hwang and M.-C. Lee, “Analysis of the Value Change of Virtual Influencers as Seen in the Press and Social Media Using Text Mining,” The Korean Journal of Advertising and Public Relations, c23(4), pp.265-299, 2021.
 N. Kumar, A. Narang and B. Lall, "Zero-Shot Normalization Driven Multi-Speaker Text to Speech Synthesis," IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 1679-1693, 2022, doi: https://doi.org/10.1109/TASLP.2022.3169634.
 D. Websdale, S. Taylor and B. Milner, "Speaker-Independent Speech Animation Using Perceptual Loss Functions and Synthetic Data," IEEE Transactions on Multimedia, vol. 24, pp. 2539-2552, 2022, doi: https://doi.org/10.1109/TMM.2021.3087020.
 Darragh Higgins, Katja Zibrek, Joao Cabral, Donal Egan, Rachel McDonnell, “Sympathy for the digital: Influence of synthetic voice on affinity, social presence and empathy for photorealistic virtual humans,” Computers & Graphics, Volume 104, pp. 116-128, ISSN 0097-8493, 2022. doi: https://doi.org/10.1016/j.cag.2022.03.009.
 B. Mones and S. Friedman, "Veering around the Uncanny Valley: Revealing the underlying structure of facial expressions," 2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG), pp. 345-345, 2011. doi: https://doi.org/10.1109/FG.2011.5771423.
 S. Racković, C. Soares, D. Jakovetić, Z. Desnica and R. Ljubobratović, "Clustering of the Blendshape Facial Model," 2021 29th European Signal Processing Conference (EUSIPCO), pp. 1556-1560, 2021. doi: https://doi.org/10.23919/EUSIPCO54536.2021.9616061.
 F. Danieau et al., "Automatic Generation and Stylization of 3D Facial Rigs," 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 784-792, 2019. doi: https://doi.org/10.1109/VR.2019.8798208.  Young-Ho Seo, Moonseok Oh, Gyu-Hoon Han, “The prese
nt and future of the digital human,” Broadcasting and Media Magazine, 26(4), pp. 72-81, 2021.
 M.Oh, G.-H. Han and Y.-H. Seo, “A Study on the Production Techniques of Digital Humans and Metahuman for Metaverse,” Design Research 6, no.3, pp. 133-142, June, 2021. doi: https://doi.org/10.46248/kidrs.2021.3.133
 M. Oh, G.-H. Han, S.-G. Park and Y.-H. Seo. “A study on analysis of graphics pipeline for 4D volumetric capturing,” Design Research 6, no.3, 2021 : 9-18. doi: https://doi.org/10.46248/kidrs.2021.3.9
 Y.-H. Seo, “Volumetric Photorealistic 4D Image Technology”, Broadcasting and Media Magazine, 26(2), pp. 56-66, April, 2021.
 B. Egger et al, “3D Morphable Face Models - Past, Present and Future,” arXivLabs, Cornell University, 2020. https://doi.org/10.48550/arXiv.1909.01815
 F. Liu, L. Tran, X. Liu., “3D Face Modeling From Diverse Raw Scan Data,” arXivLabs, Cornell University, 2019.
 B. Moghaddam, J. Lee, H. Pfister and Raghu Machiraju, “Model-based 3D face capture with shape-from-silhouettes,” 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443), pp. 20-27, 2003. doi: https://doi.org/10.1109/AMFG.2003.1240819