DOI QR코드

DOI QR Code

A Study on the Realization of Virtual Simulation Face Based on Artificial Intelligence

  • Zheng-Dong Hou (Department of Visual Contents, Dongseo University) ;
  • Ki-Hong Kim (Department of Visual Animation, Dongseo University) ;
  • Gao-He Zhang (Department of Visual Contents, Dongseo University) ;
  • Peng-Hui Li (Department of Visual Contents, Dongseo University)
  • Received : 2022.10.20
  • Accepted : 2023.01.06
  • Published : 2023.06.30

Abstract

In recent years, as computer-generated imagery has been applied to more industries, realistic facial animation is one of the important research topics. The current solution for realistic facial animation is to create realistic rendered 3D characters, but the 3D characters created by traditional methods are always different from the actual characters and require high cost in terms of staff and time. Deepfake technology can achieve the effect of realistic faces and replicate facial animation. The facial details and animations are automatically done by the computer after the AI model is trained, and the AI model can be reused, thus reducing the human and time costs of realistic face animation. In addition, this study summarizes the way human face information is captured and proposes a new workflow for video to image conversion and demonstrates that the new work scheme can obtain higher quality images and exchange effects by evaluating the quality of No Reference Image Quality Assessment.

Keywords

References

  1. L. Dzelzkaleja, J. K. Knets, N. Rozenovskis, and A. Silitis, "Mobile apps for 3D face scanning," Proceedings of SAI Intelligent Systems Conference. pp. 34-50, 2022. DOI: 10.1007/978-3-030-82196-8_4.
  2. P. Amornvit and S. Sanohkan, "The accuracy of digital face scans obtained from 3D scanners: an in vitro study," International Journal of Environmental Research and Public Health, vol. 16, no. 24, p. 5061, Dec. 2019. DOI: 10.3390/ijerph16245061.
  3. Z. Wang, "Robust three-dimensional face reconstruction by one-shot structured light line pattern," Optics and Lasers in Engineering, vol. 124, p. 105798, Jan. 2020. DOI: 10.1016/j.optlaseng.2019.105798.
  4. A. Richard, C. Lea, S. Ma, J. Gall, F. de la Torre, and Y. Sheikh, "Audio-and gaze-driven facial animation of codec avatars," in Proceedings of the IEEE/CVF winter conference on applications of computer vision. Waikoloa, USA, Jan. 2021. DOI: 10.1109/wacv48630.2021.00009.
  5. V. Barrielle, and N. Stoiber, "Realtime performance-driven physical simulation for facial animation," in Computer Graphics Forum, vol. 38, no. 1, Feb. 2019. pp. 151-166. DOI: 10.1111/cgf.13450.
  6. T-N. Nguyen, S. Dakpe, M-C. Ho Ba Tho, and T.-T. Dao, "Real-time computer vision system for tracking simultaneously subject-specific rigid head and non-rigid facial mimic movements using a contactless sensor and system of systems approach," Computer Methods and Programs in Biomedicine, vol. 191, p. 105410, Jul. 2020. DOI:10.1016/j.cmpb.2020.105410.
  7. Y. Pan, R. Zhang, J. Wang, N. Chen, Y. Qiu, Y. Ding, and K. Mitchell, "MienCap: Performance-based facial animation with live mood dynamics," in 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Christchurch, New Zealand, pp. 654-655, 2022. DOI: 10.1109/VRW55335.2022.00178.
  8. Y. Ye, S. Zhan, and Z. Juan, "High-fidelity 3D real-time facial animation using infrared structured light sensing system," Computers & Graphics, vol. 104, pp. 46-58, May. 2022. DOI: 10.1016/j.cag.2022.03.007.
  9. K. Gu, Y. Zhou, and T. Huang, "Flnet: Landmark driven fetching and learning network for faithful talking facial animation synthesis," Proceedings of the AAAI conference on artificial intelligence, vol. 34, no. 07, pp. 10861-10868, 2020. DOI: 10.1609/aaai.v34i07.6717.
  10. K. Vougioukas, S. Petridis, and M. Pantic, "End-to-end speech-driven realistic facial animation with temporal GANs," CVPR Workshops, pp. 37-40, 2019.
  11. S. W. Bailey, D. Omens, P. Dilorenzo, and J. F. O'Brien, "Fast and deep facial deformations," ACM Transactions on Graphics, vol. 39, no. 4, Aug. 2020. DOI: 10.1145/3386569.3392397.
  12. P. Chandran, D. Bradley, M. Gross, and T. Beeler, "Semantic deep face models," in 2020 International Conference on 3D Vision (3DV), Fukuoka, Japan, pp. 345-354, 2020. DOI: 10.1109/3DV50981.2020.00044.
  13. T. Karras, T. Aila, S. Laine, A. Herva, and J. Lehtinen, "Audiodriven facial animation by joint end-to-end learning of pose and emotion," ACM Transactions on Graphics, vol. 36, no. 4, pp. 1-12, Jul. 2017. DOI: 10.1145/3072959.3073658.
  14. T. T. Nguyen, C. M. Nguyen, T. D. Nguyen, T. Duc, S. Nahavandi, "Deep learning for deepfakes creation and detection," arXiv preprint arXiv:1909.11573, vol. 1, no. 2, p. 2, Sep. 2019.
  15. A. Tewari, M. Zollhoefer, F. Bernard, P. Garrido, H. Kim, P. Perez, and C. Theobalt, "High-fidelity monocular face reconstruction based on an unsupervised model-based face autoencoder," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 2, pp. 357-370, Feb. 2020. DOI: 10.1109/TPAMI.2018.2876842.
  16. J. Lin, Y. Li, and G. Yang, "FPGAN: Face deidentification method with generative adversarial networks for social robots," Neural Networks, vol. 133, pp. 132-147, Jan. 2021. DOI: 10.1016/j.neunet.2020.09.001.
  17. M-Y. Liu, X. Huang, J. Yu, T-C. Wang, and A. Mallya, "Generative adversarial networks for image and video synthesis: Algorithms and applications," Proceedings of the IEEE, vol. 109, no. 5, pp. 839-862, May 2021. DOI: 10.1109/JPROC.2021.3049196.
  18. T. T. Nguyen, Q. V. H. Nguyen, D. T. Nguyen, D. T. Nguyen, T. Huynh-The, S. Nahavandi, T. T. Nguyen, Q-V. Pham, and C. M. Nguyen, "Deep learning for deepfakes creation and detection: A survey," Computer Vision and Image Understanding, vol. 223, p. 103525, Oct. 2022. DOI: 10.1016/j.cviu.2022.103525.
  19. I. Perov, D. Gao, N. Chervoniy, K. Liu, S. Marangonda, C. Ume, Mr. Dpfks, C. S. Facenheim, L. RP, J. Jiang, S. Zhang, P. Wu, B. Zhou, and W. Zhang, "DeepFaceLab: Integrated, flexible and extensible face-swapping framework," arXiv preprint arXiv:2005.05535, May 2020. DOI: 10.48550/arXiv.2005.05535.
  20. F. Jia and S. Yang, "Video face swap with DeepFaceLab," in International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), Harbin, China, vol. 12168, pp. 326-332, Mar. 2022. DOI: 10.1117/12.2631297.
  21. L. Her and X. Yang, "Research of image sharpness assessment algorithm for autofocus," in 2019 IEEE 4th International Conference on Image, Vision, and Computing (ICIVC), IEEE, Xiamen, China, pp. 93-98, 2019. DOI: 10.1109/ICIVC47709.2019.8980980.
  22. M. K. Rohil, N. Gupta, and P. Yadav, "An improved model for no-reference image quality assessment and a no-reference video quality assessment model based on frame analysis," Signal, Image and Video Processing, vol. 14. No. 1, pp. 205-213, Feb. 2020. DOI: 10.1007/s11760-019-01543-z.
  23. X. Zhou, J. Zhang, M. Li, X. Su, F. Chen, "Thermal infrared spectrometer on-orbit defocus assessment based on blind image blur kernel estimation," Infrared Physics & Technology, vol. 130 p. 104538, May 2022. DOI: 10.1016/j.infrared.2022.104538.
  24. J. Rajevenceltha and V. H. Gaidhane, "An efficient approach for no-reference image quality assessment based on statistical texture and structural features," Engineering Science and Technology, an International Journal, vol. 30, p. 101039, Jun. 2022. DOI: 10.1016/j.jestch.2021.07.002.
  25. J. Harder, "What other programs that are part of Adobe Creative Cloud can I use to display my graphics or multimedia online?," in Graphics and Multimedia for the Web with Adobe Creative Cloud, Apress, Berkeley, CA, pp. 993-1000, Nov. 2018. DOI: 10.1007/978-1-4842-3823-3_40.
  26. X. Wu, P. Qu, S. Wang, L. Xie, J. Dong, "Extend the FFmpeg framework to analyze media content," arXiv preprint arXiv:2103. 03539, Mar. 2021. DOI: 10.48550/arXiv.2103.03539.
  27. M. Gupta, S. Shah, and S. Salmani, "Improving whatsapp Video Statuses using FFMPEG and Software based encoding," in 2021 International Conference on Communication information and Computing Technology (ICCICT), IEEE, Mumbai, India, pp. 1-6, 2021. DOI: 10.1109/ICCICT50803.2021.9510129.
  28. C. Bartneck, D. Kulic, E. Croft, et al., "Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots," International Journal of Social Robotics, vol. 1, no. 1, pp. 71-81, Jan. 2009. DOI: 10.1007/s12369-008-0001-3.