Journal of the Korea Computer Graphics Society (한국컴퓨터그래픽스학회논문지)

Korea Computer Graphics Society (한국컴퓨터그래픽스학회)
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Drape Simulation Estimation for Non-Linear Stiffness Model

비선형 강성 모델을 위한 드레이프 시뮬레이션 결과 추정

  • Received : 2023.06.17
  • Accepted : 2023.07.05
  • Published : 2023.07.25
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Abstract

In the development of clothing design through virtual simulation, it is essential to minimize the differences between the virtual and the real world as much as possible. The most critical task to enhance the similarity between virtual and real garments is to find simulation parameters that can closely emulate the physical properties of the actual fabric in use. The simulation parameter optimization process requires manual tuning by experts, demanding high expertise and a significant amount of time. Especially, considerable time is consumed in repeatedly running simulations to check the results of applying the tuned simulation parameters. Recently, to tackle this issue, artificial neural network learning models have been proposed that swiftly estimate the results of drape test simulations, which are predominantly used for parameter tuning. In these earlier studies, relatively simple linear stiffness models were used, and instead of estimating the entirety of the drape mesh, they estimated only a portion of the mesh and interpolated the rest. However, there is still a scarcity of research on non-linear stiffness models, which are commonly used in actual garment design. In this paper, we propose a learning model for estimating the results of drape simulations for non-linear stiffness models. Our learning model estimates the full high-resolution mesh model of drape. To validate the performance of the proposed method, experiments were conducted using three different drape test methods, demonstrating high accuracy in estimation.

가상 시뮬레이션을 이용한 의류 디자인 개발에서는 가상과 실제의 차이가 최소화되어야 한다. 가상 의상과 실제 의상의 유사성을 높이는 데에 가장 기본이 되는 작업은 의상 제작에 사용될 옷감의 물성을 최대한 유사하게 표현할 수 있는 시뮬레이션 파라미터를 찾는 것이다. 시뮬레이션 파라미터 최적화 절차에는 전문가의 수작업으로 이루어지는 튜닝 과정이 포함되는데, 이 작업은 높은 전문성과 많은 시간이 요구된다. 특히 조정된 시뮬레이션 파라미터를 적용한 결과를 다시 확인하기 위해 시뮬레이션을 반복적으로 실행할 때 많은 시간이 소요된다. 최근 이 문제를 해결하기 위해 파라미터 튜닝에 주로 사용되는 드레이프 테스트 시뮬레이션 결과를 빠르게 추정하는 인공신경망 학습 모델이 제안되었다. 하지만 기존 연구에서는 비교적 간단한 선형 강성 모델을 사용하였으며 드레이프 시뮬레이션 전체를 추정하는 대신 일부만 추정하고 나머지는 보간하는 방식을 사용하였다. 실제 의류 디자인 개발 과정에서는 주로 비선형 강성 모델이 적용된 시뮬레이터가 사용되지만, 이에 대한 연구는 아직 부족하다. 본 논문에서는 비선형 강성 모델을 대상으로 드레이프 시뮬레이션 결과를 추정하기 위한 새로운 학습 모델을 제안한다. 본 연구에서 제안된 학습 모델은 시뮬레이션 결과인 고해상도 메시 모델 전체를 추정한다. 제시하는 방법의 성능을 검증하기 위해 세 가지 드레이프 테스트 방식을 대상으로 실험을 진행하여 추정 정확도를 평가한다.

Keywords

Acknowledgement

본 연구는 일부 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구(No. 2021R1F1A1048002) 입니다.

References

  1. G. E. Cusick, "The dependence of fabric drape on bending and shear stiffness," Journal of the Textile Institute Transactions, vol. 56, no. 11, pp. T596-T606, 1965.  https://doi.org/10.1080/19447026508662319
  2. E. Ju, K.-y. Kim, J. Lee, S. Yoon, and M. G. Choi, "Interactive exploration of drapes by simulation parameters," Computer Animation and Virtual Worlds, vol. 33, no. 3-4, p. e2058, 2022. 
  3. S. Kawabata and M. Niwa, "Fabric performance in clothing and clothing manufacture," Journal of the Textile Institute, vol. 80, no. 1, pp. 19-50, 1989.  https://doi.org/10.1080/00405008908659184
  4. S. Kuijpers, C. Luible-Bar, and H. Gong, "The measurement of fabric properties for virtual simulation-a critical review," IEEE SA INDUSTRY CONNECTIONS, pp. 1-43, 2020. 
  5. K. S. Bhat, C. D. Twigg, J. K. Hodgins, P. K. Khosla, Z. Popovic, and S. M. Seitz, "Estimating cloth simulation parameters from video," in Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation, 2003, pp. 37-51. 
  6. H. Wang, J. F. O'Brien, and R. Ramamoorthi, "Data-driven elastic models for cloth: modeling and measurement," ACM transactions on graphics (TOG), vol. 30, no. 4, pp. 1-12, 2011.  https://doi.org/10.1145/2010324.1964966
  7. E. Miguel, D. Bradley, B. Thomaszewski, B. Bickel, W. Matusik, M. A. Otaduy, and S. Marschner, "Data-driven estimation of cloth simulation models," in Computer Graphics Forum, vol. 31, no. 2pt2. Wiley Online Library, 2012, pp. 519-528.  https://doi.org/10.1111/j.1467-8659.2012.03031.x
  8. S. Yang, Z. Pan, T. Amert, K. Wang, L. Yu, T. Berg, and M. C. Lin, "Physics-inspired garment recovery from a singleview image," ACM Transactions on Graphics (TOG), vol. 37, no. 5, pp. 1-14, 2018.  https://doi.org/10.1145/3026479
  9. K. L. Bouman, B. Xiao, P. Battaglia, and W. T. Freeman, "Estimating the material properties of fabric from video," in Proceedings of the IEEE international conference on computer vision, 2013, pp. 1984-1991. 
  10. S. Yang, J. Liang, and M. C. Lin, "Learning-based cloth material recovery from video," in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 4383-4393. 
  11. W. Bi, P. Jin, H. Nienborg, and B. Xiao, "Estimating mechanical properties of cloth from videos using dense motion trajectories: Human psychophysics and machine learning," Journal of vision, vol. 18, no. 5, pp. 12-12, 2018.  https://doi.org/10.1167/18.5.12
  12. E. Ju and M. G. Choi, "Estimating cloth simulation parameters from a static drape using neural networks," IEEE Access, vol. 8, pp. 195 113-195 121, 2020.  https://doi.org/10.1109/ACCESS.2020.3033765
  13. C. Rodriguez-Pardo, M. Prieto-Martin, D. Casas, and E. Garces, "How will it drape like? capturing fabric mechanics from depth images," arXiv preprint arXiv:2304.06704, 2023. 
  14. D. Baraff and A. Witkin, "Large steps in cloth simulation," in Proceedings of the 25th annual conference on Computer graphics and interactive techniques, 1998, pp. 43-54. 
  15. P. Volino, N. Magnenat-Thalmann, and F. Faure, "A simple approach to nonlinear tensile stiffness for accurate cloth simulation," ACM Transactions on Graphics, vol. 28, no. 4, pp. Article-No, 2009. 
  16. CLO Virtual Fashion Inc., "CLO3D," https://www.clo3d.com/, 2009-2023. 
  17. K.-J. Choi and H.-S. Ko, "Stable but responsive cloth," ACM Trans. Graph., vol. 21, no. 3, p. 604-611, jul 2002.  https://doi.org/10.1145/566654.566624
  18. X. Feng, W. Huang, W. Xu, and H. Wang, "Learning-based bending stiffness parameter estimation by a drape tester," ACM Trans. Graph., vol. 41, no. 6, 2022. 
  19. Alvanon Inc., "ALVANON," https://www.alvanon.com/, 2007-2023. 
  20. S. Gong, L. Chen, M. Bronstein, and S. Zafeiriou, "Spiralnet++: A fast and highly efficient mesh convolution operator," in Proceedings of the IEEE/CVF international conference on computer vision workshops, 2019, pp. 0-0. 
  21. J. M. Kaldor, D. L. James, and S. Marschner, "Simulating knitted cloth at the yarn level," in ACM SIGGRAPH 2008 papers, 2008, pp. 1-9. 
  22. G. Cirio, J. Lopez-Moreno, and M. A. Otaduy, "Yarn-level cloth simulation with sliding persistent contacts," IEEE transactions on visualization and computer graphics, vol. 23, no. 2, pp. 1152-1162, 2016.  https://doi.org/10.1109/TVCG.2016.2592908
  23. C. Jiang, T. Gast, and J. Teran, "Anisotropic elastoplasticity for cloth, knit and hair frictional contact," ACM Transactions on Graphics (TOG), vol. 36, no. 4, pp. 1-14, 2017. https://doi.org/10.1145/3072959.3073623