“Learning Contact Deformations with General Collider Descriptors” by Romero, Casas, Chiaramonte and Otaduy – ACM SIGGRAPH HISTORY ARCHIVES

“Learning Contact Deformations with General Collider Descriptors” by Romero, Casas, Chiaramonte and Otaduy

  • 2023 SA_Technical_Papers_Romero_Learning Contact Deformations with General Collider Descriptors

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Title:

    Learning Contact Deformations with General Collider Descriptors

Session/Category Title:   Neural Physics


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Abstract:


    This paper presents a learning-based method for the simulation of rich contact deformations on reduced deformation models. Previous works learn deformation models for specific pairs of objects, and we lift this limitation by designing a neural model that supports general rigid collider shapes. We do this by formulating a novel collider descriptor that characterizes local collider geometry in a region of interest. The paper shows that the learning-based deformation model can be trained on a library of colliders, but it accurately supports unseen collider shapes at runtime. We showcase our method on interactive dynamic simulations with animation of rich deformation detail, manipulation and exploration of untrained objects, and augmentation of contact information suitable for high-fidelity haptics.

References:


    [1]
    Noam Aigerman, Kunal Gupta, Vladimir G. Kim, Siddhartha Chaudhuri, Jun Saito, and Thibault Groueix. 2022. Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes. ACM Trans. Graph. 41, 4, Article 109 (2022), 17 pages.

    [2]
    Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, and Allan D. Jepson. 2022. Representing 3D Shapes With Probabilistic Directed Distance Fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 19343–19354.

    [3]
    Stephen W. Bailey, Dave Otte, Paul Dilorenzo, and James F. O’Brien. 2018. Fast and Deep Deformation Approximations. ACM Trans. Graph. 37, 4 (2018).

    [4]
    Hugo Bertiche, Meysam Madadi, and Sergio Escalera. 2022. Neural Cloth Simulation. ACM Trans. Graph. 41, 6, Article 220 (2022), 14 pages.

    [5]
    Christopher Brandt, Elmar Eisemann, and Klaus Hildebrandt. 2018. Hyper-Reduced Projective Dynamics. ACM Trans. Graph. 37, 4, Article 80 (2018).

    [6]
    Xinhao Cai, Eulalie Coevoet, Alec Jacobson, and Paul Kry. 2022. Active Learning Neural C-space Signed Distance Fields for Reduced Deformable Self-Collision. In Graphics Interface 2022. https://openreview.net/forum?id=r3G_ReFNpM9

    [7]
    Dan Casas and Miguel A Otaduy. 2018. Learning nonlinear soft-tissue dynamics for interactive avatars. Proceedings of the ACM on Computer Graphics and Interactive Techniques 1, 1 (2018), 10.

    [8]
    Rohan Chabra, Jan E. Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe. 2020. Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction. In Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX (Glasgow, United Kingdom). Springer-Verlag, Berlin, Heidelberg, 608–625.

    [9]
    Peter Yichen Chen, Jinxu Xiang, Dong Heon Cho, Yue Chang, G A Pershing, Henrique Teles Maia, Maurizio M Chiaramonte, Kevin Thomas Carlberg, and Eitan Grinspun. 2023. CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations. In The Eleventh International Conference on Learning Representations. https://openreview.net/forum?id=FUORz1tG8Og

    [10]
    Ethan Chun, Yilun Du, Anthony Simeonov, Tomas Lozano-Perez, and Leslie Kaelbling. 2023. Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation. arxiv:2302.03573 [cs.RO]

    [11]
    Lawson Fulton, Vismay Modi, David Duvenaud, David I. W. Levin, and Alec Jacobson. 2019. Latent-space Dynamics for Reduced Deformable Simulation. Computer Graphics Forum 38, 2 (2019), 379–391.

    [12]
    Paul Guerrero, Yanir Kleiman, Maks Ovsjanikov, and Niloy J. Mitra. 2018. PCPNet: Learning Local Shape Properties from Raw Point Clouds. Computer Graphics Forum 37, 2 (2018), 75–85. https://doi.org/10.1111/cgf.13343

    [13]
    David Harmon and Denis Zorin. 2013. Subspace Integration with Local Deformations. ACM Trans. Graph. 32, 4, Article 107 (July 2013), 10 pages.

    [14]
    Daniel Holden, Bang Chi Duong, Sayantan Datta, and Derek Nowrouzezahrai. 2019. Subspace Neural Physics: Fast Data-Driven Interactive Simulation. In Proceedings of the 18th Annual ACM SIGGRAPH/Eurographics Symposium on Computer Animation.

    [15]
    Trevor Houchens, Cheng-You Lu, Shivam Duggal, Rao Fu, and Srinath Sridhar. 2022. NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation. arxiv:2206.05837 [cs.CV]

    [16]
    C. Kane, J. E. Marsden, M. Ortiz, and M. West. 2000. Variational integrators and the Newmark algorithm for conservative and dissipative mechanical systems. Internat. J. Numer. Methods Engrg. 49, 10 (2000), 1295–1325.

    [17]
    Kookjin Lee and Kevin T. Carlberg. 2021. Deep Conservation: A Latent-Dynamics Model for Exact Satisfaction of Physical Conservation Laws. In AAAI.

    [18]
    Minchen Li, Zachary Ferguson, Teseo Schneider, Timothy Langlois, Denis Zorin, Daniele Panozzo, Chenfanfu Jiang, and Danny M. Kaufman. 2020. Incremental Potential Contact: Intersection-and Inversion-Free, Large-Deformation Dynamics. ACM Trans. Graph. 39, 4, Article 49 (2020), 20 pages.

    [19]
    Xinhai Liu, Zhizhong Han, Yu-Shen Liu, and Matthias Zwicker. 2019. Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network. In AAAI Conference on Artificial Intelligence.

    [20]
    R. Luo, T. Shao, H. Wang, W. Xu, X. Chen, K. Zhou, and Y. Yang. 2020. NNWarp: Neural Network-Based Nonlinear Deformation. IEEE Transactions on Visualization and Computer Graphics 26, 4 (2020), 1745–1759.

    [21]
    Qing Lyu, Menglei Chai, Xiang Chen, and Kun Zhou. 2022. Real-Time Hair Simulation With Neural Interpolation. IEEE Transactions on Visualization and Computer Graphics 28, 4 (2022), 1894–1905. https://doi.org/10.1109/TVCG.2020.3029823

    [22]
    Ning Ni, Qingyu Xu, Zhehao Li, Xiao-Ming Fu, and Ligang Liu. 2023. Numerical Coarsening with Neural Shape Functions. Computer Graphics Forum n/a, n/a (2023).

    [23]
    Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, and Peter Battaglia. 2021. Learning Mesh-Based Simulation with Graph Networks. In International Conference on Learning Representations. https://openreview.net/forum?id=roNqYL0_XP

    [24]
    Adrien Poulenard and Maks Ovsjanikov. 2018. Multi-Directional Geodesic Neural Networks via Equivariant Convolution. ACM Trans. Graph. 37, 6, Article 236 (2018), 14 pages.

    [25]
    Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. 2017a. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proc. of Computer Vision and Pattern Recognition (CVPR). 652–660.

    [26]
    Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. 2017b. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Advances in Neural Information Processing Systems (NeurIPS) 30 (2017).

    [27]
    Cristian Romero, Dan Casas, Maurizio M. Chiaramonte, and Miguel A. Otaduy. 2022. Contact-Centric Deformation Learning. ACM Trans. Graph. 41, 4, Article 70 (2022), 11 pages.

    [28]
    Cristian Romero, Dan Casas, Jesús Pérez, and Miguel Otaduy. 2021. Learning Contact Corrections for Handle-Based Subspace Dynamics. ACM Trans. Graph. 40, 4 (2021).

    [29]
    Igor Santesteban, Elena Garces, Miguel A. Otaduy, and Dan Casas. 2020. SoftSMPL: Data-driven Modeling of Nonlinear Soft-tissue Dynamics for Parametric Humans. Computer Graphics Forum 39, 2 (2020), 65–75.

    [30]
    Igor Santesteban, Miguel Otaduy, Nils Thuerey, and Dan Casas. 2022b. ULNeF: Untangled Layered Neural Fields for Mix-and-Match Virtual Try-On. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.). Vol. 35. Curran Associates, Inc., 12110–12125. https://proceedings.neurips.cc/paper_files/paper/2022/file/4ee3ac2cd119023c79b0d21c4a464dc7-Paper-Conference.pdf

    [31]
    Igor Santesteban, Miguel A. Otaduy, and Dan Casas. 2022a. SNUG: Self-Supervised Neural Dynamic Garments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 8140–8150.

    [32]
    Igor Santesteban, Nils Thuerey, Miguel A Otaduy, and Dan Casas. 2021. Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021).

    [33]
    Nicholas Sharp, Cristian Romero, Alec Jacobson, Etienne Vouga, Paul G. Kry, David I. W. Levin, and Justin Solomon. 2023. Data-Free Learning of Reduced-Order Kinematics. arxiv:2305.03846 [cs.GR]

    [34]
    Siyuan Shen, Yin Yang, Tianjia Shao, He Wang, Chenfanfu Jiang, Lei Lan, and Kun Zhou. 2021. High-Order Differentiable Autoencoder for Nonlinear Model Reduction. ACM Trans. Graph. 40, 4, Article 68 (2021).

    [35]
    Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, and Vincent Sitzmann. 2022. Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation. In 2022 International Conference on Robotics and Automation (ICRA). 6394–6400. https://doi.org/10.1109/ICRA46639.2022.9812146

    [36]
    Breannan Smith, Fernando De Goes, and Theodore Kim. 2018. Stable Neo-Hookean Flesh Simulation. ACM Trans. Graph. 37, 2, Article 12 (March 2018), 15 pages.

    [37]
    A. H. Stroud. 1971. Approximate calculation of multiple integrals. Prentice-Hall.

    [38]
    Qingyang Tan, Zherong Pan, Breannan Smith, Takaaki Shiratori, and Dinesh Manocha. 2022a. N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations. In Proceedings of the 39th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 162), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (Eds.). PMLR, 21037–21049.

    [39]
    Qingyang Tan, Yi Zhou, Tuanfeng Wang, Duygu Ceylan, Xin Sun, and Dinesh Manocha. 2022b. A Repulsive Force Unit for Garment Collision Handling in Neural Networks. In Computer Vision – ECCV 2022, Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, and Tal Hassner (Eds.). Springer Nature Switzerland, Cham, 451–467.

    [40]
    Mickeal Verschoor, Dan Casas, and Miguel A. Otaduy. 2020. Tactile Rendering Based on Skin Stress Optimization. ACM Trans. Graph. 39, 4, Article 90 (2020).

    [41]
    Yu Wang, Alec Jacobson, Jernej Barbič, and Ladislav Kavan. 2015. Linear Subspace Design for Real-Time Shape Deformation. ACM Trans. Graph. 34, 4, Article 57 (2015).

    [42]
    Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon. 2019. Dynamic Graph CNN for Learning on Point Clouds. ACM Transactions on Graphics (TOG) (2019).

    [43]
    Ruben Wiersma, Elmar Eisemann, and Klaus Hildebrandt. 2020. CNNs on Surfaces Using Rotation-Equivariant Features. ACM Trans. Graph. 39, 4, Article 92 (2020), 12 pages.

    [44]
    Lingchen Yang, Byungsoo Kim, Gaspard Zoss, Baran Gözcü, Markus Gross, and Barbara Solenthaler. 2022. Implicit Neural Representation for Physics-Driven Actuated Soft Bodies. ACM Trans. Graph. 41, 4, Article 122 (2022), 10 pages.

    [45]
    Mianlun Zheng, Yi Zhou, Duygu Ceylan, and Jernej Barbic. 2021. A Deep Emulator for Secondary Motion of 3D Characters. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5932–5940.

    [46]
    Qingnan Zhou and Alec Jacobson. 2016. Thingi10K: A Dataset of 10,000 3D-Printing Models. arxiv:1605.04797 [cs.GR]


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