Abstract
Shape deformation is an important component in any geometry processing toolbox. The goal is to enable intuitive deformations of single or multiple shapes or to transfer example deformations to new shapes while preserving the plausibility of the deformed shape(s). Existing approaches assume access to point-level or part-level correspondence or establish them in a preprocessing phase, thus limiting the scope and generality of such approaches. We propose DeformSyncNet, a new approach that allows consistent and synchronized shape deformations without requiring explicit correspondence information. Technically, we achieve this by encoding deformations into a class-specific idealized latent space while decoding them into an individual, model-specific linear deformation action space, operating directly in 3D. The underlying encoding and decoding are performed by specialized (jointly trained) neural networks. By design, the inductive bias of our networks results in a deformation space with several desirable properties, such as path invariance across different deformation pathways, which are then also approximately preserved in real space. We qualitatively and quantitatively evaluate our framework against multiple alternative approaches and demonstrate improved performance.
Supplemental Material
- Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas. 2018. Learning Representations and Generative Models for 3D Point Clouds. In ICML.Google Scholar
- Panos Achlioptas, Judy Fan, X.D. Robert Hawkins, D. Noah Goodman, and J. Leonidas Guibas. 2019. ShapeGlot: Learning Language for Shape Differentiation. In ICCV.Google Scholar
- Ilya Baran, Daniel Vlasic, Eitan Grinspun, and Jovan Popovic. 2009. Semantic Deformation Transfer. In ACM SIGGRAPH.Google Scholar
- Mirela Ben-Chen, Ofir Weber, and Craig Gotsman. 2009. Spatial Deformation Transfer. In ACM SIGGRAPH/Eurographics Symposium on Computer Animation.Google Scholar
- Angel X. Chang, Thomas A. Funkhouser, Leonidas J. Guibas, Pat Hanrahan, Qi-Xing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. 2015. ShapeNet: An Information-Rich 3D Model Repository. arXiv:1512.03012Google Scholar
- Lu Chen, Jin Huang, Hanqiu Sun, and Hujun Bao. 2010. Cage-based deformation transfer. Computer & Graphics (2010).Google Scholar
- Dawson-Haggerty et al. [n.d.]. trimesh. https://trimsh.org/Google Scholar
- Chris Ding, Ding Zhou, Xiaofeng He, and Hongyuan Zha. 2006. R1-PCA: Rotational Invariant L1-Norm Principal Component Analysis for Robust Subspace Factorization. In ICML.Google Scholar
- Haoqiang Fan, Hao Su, and Leonidas Guibas. 2017. A Point Set Generation Network for 3D Object Reconstruction from a Single Image. In CVPR.Google Scholar
- Noa Fish, Melinos Averkiou, Oliver van Kaick, Olga Sorkine-Hornung, Daniel Cohen-Or, and Niloy J. Mitra. 2014. Meta-representation of Shape Families. In ACM SIGGRAPH.Google Scholar
- Ran Gal, Olga Sorkine, Niloy J. Mitra, and Daniel Cohen-Or. 2009. iWIRES: an analyze-and-edit approach to shape manipulation. In ACM SIGGRAPH.Google Scholar
- Jean Gallier. 2011. Geometric Methods and Applications. Springer.Google ScholarDigital Library
- Lin Gao, Jie Yang, Yi-Ling Qiao, Yu-Kun Lai, Paul L. Rosin, Weiwei Xu, and Shihong Xia. 2018. Automatic Unpaired Shape Deformation Transfer. In ACM SIGGRAPH Asia.Google Scholar
- Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William T. Freeman, and Thomas Funkhouser. 2019. Learning Shape Templates with Structured Implicit Functions. In ICCV.Google Scholar
- Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan Russell, and Mathieu Aubry. 2018. AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation. In CVPR.Google Scholar
- Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, and Mathieu Aubry. 2019. Deep Self-Supervised Cycle-Consistent Deformation for Few-Shot Shape Segmentation. In Eurographics Symposium on Geometry Processing.Google Scholar
- Rana Hanocka, Noa Fish, Zhenhua Wang, Raja Giryes, Shachar Fleishman, and Daniel Cohen-Or. 2018. ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning. ACM Transactions on Graphics (2018).Google ScholarDigital Library
- Aaron Hertzmann, Charles E. Jacobs, Nuria Oliver, Brian Curless, and David H. Salesin. 2001. Image Analogies. In ACM SIGGRAPH Asia.Google Scholar
- Haibin Huang, Evangelos Kalogerakis, Siddhartha Chaudhuri, Duygu Ceylan, Vladimir G. Kim, and Ersin Yumer. 2017. Learning Local Shape Descriptors from Part Correspondences with Multiview Convolutional Networks. ACM Transactions on Graphics (2017).Google Scholar
- Ruqi Huang, Panos Achlioptas, Leonidas Guibas, and Maks Ovsjanikov. 2019a. Limit Shapes-A Tool for Understanding Shape Differences and Variability in 3D Model Collections. In Eurographics Symposium on Geometry Processing.Google ScholarCross Ref
- Ruqi Huang, Marie-Julie Rakotosaona, Panos Achlioptas, Leonidas J. Guibas, and Maks Ovsjanikov. 2019b. OperatorNet: Recovering 3D Shapes From Difference Operators. In ICCV.Google Scholar
- Takeo Igarashi, Tomer Moscovich, and John F. Hughes. 2005. As-Rigid-as-Possible Shape Manipulation. In ACM SIGGRAPH.Google Scholar
- Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. 2017. Image-to-Image Translation with Conditional Adversarial Networks. In CVPR.Google Scholar
- Dominic Jack, Jhony K. Pontes, Sridha Sridharan, Clinton Fookes, Sareh Shirazi, Frederic Maire, and Anders Eriksson. 2018. Learning Free-Form Deformations for 3D Object Reconstruction. In ICCV.Google Scholar
- Vladimir G. Kim, Wilmot Li, Niloy J. Mitra, Siddhartha Chaudhuri, Stephen DiVerdi, and Thomas Funkhouser. 2013. Learning Part-based Templates from Large Collections of 3D Shapes. In ACM SIGGRAPH.Google Scholar
- Andrey Kurenkov, Jingwei Ji, Animesh Garg, Viraj Mehta, JunYoung Gwak, Christopher Bongsoo Choy, and Silvio Savarese. 2018. DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image. In WACV.Google Scholar
- Hao Li, Robert W. Sumner, and Mark Pauly. 2008. Global Correspondence Optimization for Non-Rigid Registration of Depth Scans. In Eurographics Symposium on Geometry Processing.Google Scholar
- Lingxiao Li, Minhyuk Sung, Anastasia Dubrovina, Li Yi, and Leonidas Guibas. 2019. Supervised Fitting of Geometric Primitives to 3D Point Clouds. In CVPR.Google Scholar
- Yaron Lipman, Olga Sorkine, Daniel Cohen-Or, and David Levin. 2005. Linear Rotation-Invariant Coordinates for Meshes. In ACM SIGGRAPH.Google Scholar
- Jerry Liu, Fisher Yu, and Thomas Funkhouser. 2017. Interactive 3D Modeling with a Generative Adversarial Network. arXiv:1706.05170Google Scholar
- Chongyang Ma, Haibin Huang, Alla Sheffer, Evangelos Kalogerakis, and Rui Wang. 2009. Analogy-driven 3D style transfer. In Eurographics.Google Scholar
- Q. McNemar. 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika (1947).Google Scholar
- Eloi Mehr, Ariane Jourdan, Nicolas Thome, Matthieu Cord, and Vincent Guitteny. 2019. DiscoNet: Shapes Learning on Disconnected Manifolds for 3D Editing. In ICCV.Google Scholar
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and Their Compositionality. In NeurIPS.Google Scholar
- Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, and Leonidas Guibas. 2019a. StructEdit: Learning Structural Shape Variations. arXiv:1911.11098Google Scholar
- Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy J. Mitra, and Leonidas Guibas. 2019b. StructureNet: Hierarchical Graph Networks for 3D Shape Generation. In ACM SIGGRAPH Asia.Google Scholar
- Kaichun Mo, Shilin Zhu, Angel X. Chang, Li Yi, Subarna Tripathi, Leonidas J. Guibas, and Hao Su. 2019c. PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding. In CVPR.Google Scholar
- Feiping Nie, Heng Huang, Xiao Cai, and Chris Ding. 2010. Efficient and Robust Feature Selection via Joint l2, 1-Norms Minimization. In NeurIPS.Google Scholar
- Maks Ovsjanikov, Wilmot Li, Leonidas Guibas, and Niloy Mitra. 2011. Exploration of Continuous Variability in Collections of 3D Shapes. In ACM SIGGRAPH.Google Scholar
- Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. 2017. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. In CVPR.Google Scholar
- Raif M. Rustamov, Maks Ovsjanikov, Omri Azencot, Mirela Ben-Chen, Frédéric Chazal, and Leonidas Guibas. 2013. Map-Based Exploration of Intrinsic Shape Differences and Variability. In ACM SIGGRAPH.Google Scholar
- Adriana Schulz, Ariel Shamir, Ilya Baran, David I.W. Levin, Pitchaya Sitthi-amorn, and Wojciech Matusik. 2017. Retrieval on Parametric Shape Collections. In ACM SIGGRAPH.Google Scholar
- Olga Sorkine, Daniel Cohen-Or, Yaron Lipman, Marc Alexa, Christian Rössl, and Hans-Peter Seidel. 2004. Laplacian Surface Editing. In Eurographics Symposium on Geometry Processing.Google Scholar
- Stratasys. [n.d.]. GrabCAD Community. https://grabcad.com/libraryGoogle Scholar
- Robert W. Sumner and Jovan Popovic. 2004. Deformation Transfer for Triangle Meshes. In ACM SIGGRAPH.Google Scholar
- Minhyuk Sung, Hao Su, Vladimir G. Kim, Siddhartha Chaudhuri, and Leonidas Guibas. 2017. ComplementMe: Weakly-supervised Component Suggestions for 3D Modeling. In ACM SIGGRAPH Asia.Google Scholar
- Minhyuk Sung, Hao Su, Ronald Yu, and Leonidas Guibas. 2018. Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions. In NeurIPS.Google Scholar
- A.R. Tarrida. 2011. Affine Maps, Euclidean Motions and Quadrics. Springer.Google Scholar
- Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, and Jiajun Wu. 2019. Learning to Infer and Execute 3D Shape Programs. In ICLR.Google Scholar
- Trimble. [n.d.]. 3D Warehouse. https://3dwarehouse.sketchup.com/Google Scholar
- Shubham Tulsiani, Hao Su, Leonidas J. Guibas, Alexei A. Efros, and Jitendra Malik. 2017. Learning Shape Abstractions by Assembling Volumetric Primitives. In CVPR.Google Scholar
- TurboSquid. [n.d.]. TurboSquid. https://www.turbosquid.com/Google Scholar
- Ruben Villegas, Jimei Yang, Duygu Ceylan, and Honglak Lee. 2018. Neural Kinematic Networks for Unsupervised Motion Retargetting. In CVPR.Google Scholar
- Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann. 2019a. 3DN: 3D Deformation Network. In CVPR.Google Scholar
- Xiaogang Wang, Bin Zhou, Yahao Shi, Xiaowu Chen, Qinping Zhao, and Kai Xu. 2019b. Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes. In CVPR.Google Scholar
- Yanzhen Wang, Kai Xu, Jun Li, Hao Zhang, Ariel Shamir, Ligang Liu, Zhi-Quan Cheng, and Y. Xiong. 2011. Symmetry Hierarchy of Man-Made Objects. In Eurographics.Google Scholar
- Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, and Joshua B. Tenenbaum. 2016. Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. In NeurIPS.Google ScholarDigital Library
- Shihong Xia, Congyi Wang, Jinxiang Chai, and Jessica Hodgins. 2015. Realtime style transfer for unlabeled heterogeneous human motion. ACM Transactions on Graphics (2015).Google Scholar
- Kai Xu, Honghua Li, Hao Zhang, Daniel Cohen-Or, Yueshan Xiong, and Zhi-Quan Cheng. 2010. Style-Content Separation by Anisotropic Part Scales. In ACM SIGGRAPH Asia.Google Scholar
- Weiwei Xu, Jun Wang, KangKang Yin, Kun Zhou, Michiel van de Panne, Falai Chen, and Baining Guo. 2009. Joint-aware Manipulation of Deformable Models. In ACM SIGGRAPH.Google Scholar
- Jie Yang, Lin Gao, Yu-Kun Lai, Paul L. Rosin, and Shihong Xia. 2018. Biharmonic deformation transfer with automatic key point selection. Graphical Models (2018).Google Scholar
- Li Yi, Leonidas Guibas, Aaron Hertzmann, Vladimir G. Kim, Hao Su, and Ersin Yumer. 2017. Learning Hierarchical Shape Segmentation and Labeling from Online Repositories. In ACM SIGGRAPH.Google Scholar
- Li Yi, Vladimir G. Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas Guibas. 2016. A Scalable Active Framework for Region Annotation in 3D Shape Collections. In ACM SIGGRAPH Asia.Google Scholar
- Wang Yifan, Noam Aigerman, Vladimir Kim, Siddhartha Chaudhuri, and Olga Sorkine-Hornung. 2020. Neural Cages for Detail-Preserving 3D Deformations. arXiv:1912.06395Google Scholar
- Kangxue Yin, Zhiqin Chen, Hui Huang, Daniel Cohen-Or, and Hao Zhang. 2019. LOGAN: Unpaired Shape Transform in Latent Overcomplete Space. In ACM SIGGRAPH Asia.Google ScholarDigital Library
- Ersin Yumer and Levent Burak Kara. 2014. Co-Constrained Handles for Deformation in Shape Collections. In ACM SIGGRAPH Asia.Google Scholar
- Ersin Yumer and Niloy J. Mitra. 2016. Learning Semantic Deformation Flows with 3D Convolutional Networks. In ECCV.Google Scholar
- Yongheng Zhao, Tolga Birdal, Haowen Deng, and Federico Tombari. 2019. 3D Point Capsule Networks. In CVPR.Google Scholar
- Youyi Zheng, Daniel Cohen-Or, Melinos Averkiou, and Niloy J. Mitra. 2014. Recurring Part Arrangements in Shape Collections. In Eurographics.Google Scholar
- Youyi Zheng, Hongbo Fu, Daniel Cohen-Or, Oscar Kin-Chung Au, and Chiew-Lan Tai. 2011. Component-wise Controllers for Structure-Preserving Shape Manipulation. In Eurographics.Google Scholar
- Kun Zhou, Weiwei Xu, Yiying Tong, and Mathieu Desbrun. 2010. Deformation Transfer to Multi-Component Objects. In Eurographics.Google Scholar
- Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. In ICCV.Google Scholar
Index Terms
- DeformSyncNet: Deformation transfer via synchronized shape deformation spaces
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