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Inverse Garment and Pattern Modeling with a Differentiable Simulator (2403.06841v3)

Published 11 Mar 2024 in cs.GR

Abstract: The capability to generate simulation-ready garment models from 3D shapes of clothed humans will significantly enhance the interpretability of captured geometry of real garments, as well as their faithful reproduction in the virtual world. This will have notable impact on fields like shape capture in social VR, and virtual try-on in the fashion industry. To align with the garment modeling process standardized by the fashion industry as well as cloth simulation softwares, it is required to recover 2D patterns. This involves an inverse garment design problem, which is the focus of our work here: Starting with an arbitrary target garment geometry, our system estimates an animatable garment model by automatically adjusting its corresponding 2D template pattern, along with the material parameters of the physics-based simulation (PBS). Built upon a differentiable cloth simulator, the optimization process is directed towards minimizing the deviation of the simulated garment shape from the target geometry. Moreover, our produced patterns meet manufacturing requirements such as left-to-right-symmetry, making them suited for reverse garment fabrication. We validate our approach on examples of different garment types, and show that our method faithfully reproduces both the draped garment shape and the sewing pattern.

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References (62)
  1. 3ds Max. Autodesk. https://www.autodesk.com/products/3ds-max/.
  2. An efficiently computable metric for comparing polygonal shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(3):209–216, 1991.
  3. Estimating garment patterns from static scan data. In Computer Graphics Forum, pages 273–287. Wiley Online Library, 2021.
  4. Physics-driven pattern adjustment for direct 3d garment editing. ACM Trans. Graph., 35(4):50–1, 2016.
  5. Shape reconstruction by learning differentiable surface representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4716–4725, 2020.
  6. Pbns: physically based neural simulator for unsupervised garment pose space deformation. arXiv preprint arXiv:2012.11310, 2020.
  7. Neural cloth simulation. ACM Trans. Graph., 41(6), 2022.
  8. Multi-garment net: Learning to dress 3d people from images. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5420–5430, 2019.
  9. Combining implicit function learning and parametric models for 3d human reconstruction. In European Conference on Computer Vision (ECCV). Springer, 2020a.
  10. Loopreg: Self-supervised learning of implicit surface correspondences, pose and shape for 3d human mesh registration. In Advances in Neural Information Processing Systems (NeurIPS), 2020b.
  11. Vox2cortex: fast explicit reconstruction of cortical surfaces from 3d mri scans with geometric deep neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20773–20783, 2022.
  12. Simulation of clothing with folds and wrinkles. In ACM SIGGRAPH 2005 Courses, pages 3–es. 2005.
  13. Garment modeling with a depth camera. ACM Transactions on Graphics (TOG), 34(6):1–12, 2015.
  14. Tightcap: 3d human shape capture with clothing tightness field. ACM Trans. Graph., 41(1), 2021.
  15. Structure-preserving 3d garment modeling with neural sewing machines. Advances in Neural Information Processing Systems, 35:15147–15159, 2022.
  16. Beyond static features for temporally consistent 3d human pose and shape from a video. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1964–1973, 2021.
  17. Smplicit: Topology-aware generative model for clothed people. In CVPR, 2021.
  18. Drapenet: Garment generation and self-supervised draping. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1451–1460, 2023.
  19. Capturing and animation of body and clothing from monocular video, 2022.
  20. Deepcap: Monocular human performance capture using weak supervision. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020.
  21. Robust treatment of simultaneous collisions. In ACM SIGGRAPH 2008 papers, pages 1–4. 2008.
  22. Chainqueen: A real-time differentiable physical simulator for soft robotics. In 2019 International conference on robotics and automation (ICRA), pages 6265–6271. IEEE, 2019.
  23. Learning to boost training by periodic nowcasting near future weights. In Proceedings of the 40th International Conference on Machine Learning, pages 14730–14757. PMLR, 2023.
  24. Adam: A method for stochastic optimization. In International Conference on Learning Representations (ICLR), 2015.
  25. Vibe: Video inference for human body pose and shape estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5253–5263, 2020.
  26. Generating datasets of 3d garments with sewing patterns. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, 2021.
  27. Neuraltailor: reconstructing sewing pattern structures from 3d point clouds of garments. ACM Transactions on Graphics (TOG), 41(4):1–16, 2022.
  28. Estimating cloth elasticity parameters using position-based simulation of compliant constrained dynamics. arXiv preprint arXiv:2212.08790, 2022.
  29. Isp: Multi-layered garment draping with implicit sewing patterns. Advances in Neural Information Processing Systems, 36, 2024.
  30. Deep physics-aware inference of cloth deformation for monocular human performance capture, 2021.
  31. Diffcloth: Differentiable cloth simulation with dry frictional contact. ACM Transactions on Graphics (TOG), 42(1):1–20, 2022.
  32. Differentiable cloth simulation for inverse problems. Advances in Neural Information Processing Systems, 32, 2019.
  33. Smpl: A skinned multi-person linear model. ACM transactions on graphics (TOG), 34(6):1–16, 2015.
  34. Learning to dress 3d people in generative clothing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6469–6478, 2020.
  35. Scale: Modeling clothed humans with a surface codec of articulated local elements. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16082–16093, 2021a.
  36. The power of points for modeling humans in clothing. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 10974–10984, 2021b.
  37. Computer aided clothing pattern design with 3d editing and pattern alteration. Computer-Aided Design, 44(8):721–734, 2012.
  38. A-sdf: Learning disentangled signed distance functions for articulated shape representation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 12981–12991, 2021.
  39. Adaptive anisotropic remeshing for cloth simulation. ACM transactions on graphics (TOG), 31(6):1–10, 2012.
  40. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165–174, 2019.
  41. Tailornet: Predicting clothing in 3d as a function of human pose, shape and garment style. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020.
  42. Computational pattern making from 3d garment models. ACM Transactions on Graphics (TOG), 41(4):1–14, 2022.
  43. Clothcap: Seamless 4d clothing capture and retargeting. ACM Transactions on Graphics (ToG), 36(4):1–15, 2017.
  44. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. arXiv preprint arXiv:1905.05172, 2019.
  45. Learning-based animation of clothing for virtual try-on. Computer Graphics Forum, 38(2):355–366, 2019.
  46. Snug: Self-supervised neural dynamic garments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8140–8150, 2022.
  47. Peter H Schönemann. A generalized solution of the orthogonal procrustes problem. Psychometrika, 31(1):1–10, 1966.
  48. Variational surface cutting. ACM Transactions on Graphics (TOG), 37(4):1–13, 2018.
  49. Fem simulation of 3d deformable solids: a practitioner’s guide to theory, discretization and model reduction. In Acm siggraph 2012 courses, pages 1–50. 2012.
  50. Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33:7462–7473, 2020.
  51. Fast continuous collision detection using deforming non-penetration filters. In Proceedings of the 2010 ACM SIGGRAPH symposium on Interactive 3D Graphics and Games, pages 7–13, 2010.
  52. Sizer: A dataset and model for parsing 3d clothing and learning size sensitive 3d clothing. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16, pages 1–18. Springer, 2020.
  53. Directional field synthesis, design, and processing. In Computer graphics forum, pages 545–572. Wiley Online Library, 2016.
  54. Fully convolutional graph neural networks for parametric virtual try-on. In Computer Graphics Forum, pages 145–156. Wiley Online Library, 2020.
  55. Huamin Wang. Rule-free sewing pattern adjustment with precision and efficiency. ACM Transactions on Graphics (TOG), 37(4):1–13, 2018.
  56. Nerf–: Neural radiance fields without known camera parameters. arXiv preprint arXiv:2102.07064, 2021.
  57. 3d custom fit garment design with body movement. arXiv preprint arXiv:2102.05462, pages 1–12, 2021.
  58. H-nerf: Neural radiance fields for rendering and temporal reconstruction of humans in motion. In Advances in Neural Information Processing Systems, pages 14955–14966. Curran Associates, Inc., 2021.
  59. Physics-inspired garment recovery from a single-view image. ACM Trans. Graph., 37(5), 2018.
  60. Mixste: Seq2seq mixed spatio-temporal encoder for 3d human pose estimation in video. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 13232–13242, 2022a.
  61. Motion guided deep dynamic 3d garments. ACM Trans. Graph., 41(6), 2022b.
  62. 3d human pose estimation with spatial and temporal transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11656–11665, 2021.

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