research-article

Neural jacobian fields: learning intrinsic mappings of arbitrary meshes

Authors:

Vladimir G. Kim,

Siddhartha Chaudhuri,

Thibault GroueixAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 41, Issue 4

Article No.: 109, Pages 1 - 17

https://doi.org/10.1145/3528223.3530141

Published: 22 July 2022 Publication History

Abstract

This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogeneous collections of meshes that do not share a triangulation, as well as producing highly detail-preserving maps whose accuracy exceeds current state of the art. The framework is based on reducing the neural aspect to a prediction of a matrix for a single given point, conditioned on a global shape descriptor. The field of matrices is then projected onto the tangent bundle of the given mesh, and used as candidate jacobians for the predicted map. The map is computed by a standard Poisson solve, implemented as a differentiable layer with cached pre-factorization for efficient training. This construction is agnostic to the triangulation of the input, thereby enabling applications on datasets with varying triangulations. At the same time, by operating in the intrinsic gradient domain of each individual mesh, it allows the framework to predict highly-accurate mappings. We validate these properties by conducting experiments over a broad range of scenarios, from semantic ones such as morphing, registration, and deformation transfer, to optimization-based ones, such as emulating elastic deformations and contact correction, as well as being the first work, to our knowledge, to tackle the task of learning to compute UV parameterizations of arbitrary meshes. The results exhibit the high accuracy of the method as well as its versatility, as it is readily applied to the above scenarios without any changes to the framework.

Supplemental Material

MP4 File

presentation

Download
260.78 MB

SRT File

presentation

Download
18.86 KB

References

[1]

Noam Aigerman and Yaron Lipman. 2013. Injective and bounded distortion mappings in 3D. ACM Transactions on Graphics (TOG) 32, 4 (2013), 1--14.

Digital Library

[2]

Dragomir Anguelov, Praveen Srinivasan, Daphne Koller, Sebastian Thrun, Jim Rodgers, and James Davis. 2005. SCAPE: Shape Completion and Animation of People. In SIGGRAPH.

Digital Library

[3]

Mathieu Aubry, Ulrich Schlickewei, and Daniel Cremers. 2011. The wave kernel signature: A quantum mechanical approach to shape analysis. In 2011 IEEE international conference on computer vision workshops (ICCV workshops). IEEE, 1626--1633.

[4]

Stephen W Bailey, Dalton Omens, Paul Dilorenzo, and James F O'Brien. 2020. Fast and deep facial deformations. ACM Transactions on Graphics (TOG) 39, 4 (2020), 94--1.

Digital Library

[5]

Stephen W. Bailey, Dave Otte, Paul Dilorenzo, and James F. O'Brien. 2018. Fast and Deep Deformation Approximations. ACM Transactions on Graphics 37, 4 (Aug. 2018), 119:1--12. Presented at SIGGRAPH 2018, Los Angeles.

Digital Library

[6]

Ilya Baran and Jovan Popović. 2007. Automatic Rigging and Animation of 3D Characters. ACM Trans. Graph. 26, 3 (jul 2007), 72--es.

Digital Library

[7]

Jan Bednarik, Shaifali Parashar, Erhan Gundogdu, Mathieu Salzmann, and Pascal Fua. 2020. Shape reconstruction by learning differentiable surface representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4716--4725.

[8]

Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter Gehler, Javier Romero, and Michael J. Black. 2016. Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image. In Computer Vision - ECCV 2016 (Lecture Notes in Computer Science). Springer International Publishing.

[9]

Federica Bogo, Javier Romero, Matthew Loper, and Michael J. Black. 2014. FAUST: Dataset and evaluation for 3D mesh registration. In CVPR.

[10]

Sofien Bouaziz, Andrea Tagliasacchi, Hao Li, and Mark Pauly. 2016. Modern Techniques and Applications for Real-Time Non-Rigid Registration. In SIGGRAPH ASIA 2016 Courses (Macau) (SA '16). Association for Computing Machinery, New York, NY, USA, Article 11, 25 pages.

Digital Library

[11]

Xingyi Du, Noam Aigerman, Qingnan Zhou, Shahar Z Kovalsky, Yajie Yan, Danny M Kaufman, and Tao Ju. 2020. Lifting simplices to find injectivity. ACM Transactions on Graphics (TOG) 39, 4 (2020), 120--1.

Digital Library

[12]

Lawson Fulton, Vismay Modi, David Duvenaud, David I. W. Levin, and Alec Jacobson. 2019. Latent-space Dynamics for Reduced Deformable Simulation. Computer Graphics Forum (2019).

[13]

Lin Gao, Jie Yang, Yi-Ling Qiao, Yu-Kun Lai, Paul L Rosin, Weiwei Xu, and Shihong Xia. 2018. Automatic Unpaired Shape Deformation Transfer. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH Asia 2018) 37, 6 (2018), To appear.

[14]

Lin Gao, Jie Yang, Tong Wu, Yu-Jie Yuan, Hongbo Fu, Yu-Kun Lai, and Hao Zhang. 2019. SDM-NET: Deep generative network for structured deformable mesh. ACM Transactions on Graphics (TOG) 38, 6 (2019), 1--15.

Digital Library

[15]

Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, and Mathieu Aubry. 2018a. 3D-CODED: 3D Correspondences by Deep Deformation. ECCV (2018).

[16]

Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. 2018b. AtlasNet: A Papier-Mache Approach to Learning 3D Surface Generation. arXiv preprint arXiv:1802.05384 (2018).

[17]

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. SGP (2019).

[18]

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. 1--12.

Digital Library

[19]

Daniel Holden, Jun Saito, and Taku Komura. 2015. Learning an Inverse Rig Mapping for Character Animation. In Proceedings of the 14th ACM SIGGRAPH / Eurographics Symposium on Computer Animation (Los Angeles, California) (SCA '15). Association for Computing Machinery, New York, NY, USA, 165--173.

Digital Library

[20]

Jingwei Huang, Chiyu Max Jiang, Baiqiang Leng, Bin Wang, and Leonidas Guibas. 2020. Meshode: A robust and scalable framework for mesh deformation. arXiv preprint arXiv:2005.11617 (2020).

[21]

Alec Jacobson, Ilya Baran, Jovan Popovic, and Olga Sorkine. 2011. Bounded biharmonic weights for real-time deformation. ACM Trans. Graph. 30, 4 (2011), 78.

Digital Library

[22]

Alec Jacobson, Zhigang Deng, Ladislav Kavan, and JP Lewis. 2014. Skinning: Real-time Shape Deformation. In ACMSIGGRAPH 2014 Courses.

[23]

Tomas Jakab, Richard Tucker, Ameesh Makadia, Jiajun Wu, Noah Snavely, and Angjoo Kanazawa. 2021. KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12783--12792.

[24]

Chiyu Jiang, Jingwei Huang, Andrea Tagliasacchi, Leonidas Guibas, et al. 2020. Shape-flow: Learnable deformations among 3d shapes. arXiv preprint arXiv:2006.07982 (2020).

[25]

Tao Ju, Scott Schaefer, and Joe Warren. 2005. Mean value coordinates for closed triangular meshes. In ACM Siggraph 2005 Papers. 561--566.

[26]

Angjoo Kanazawa, Shahar Kovalsky, Ronen Basri, and David Jacobs. 2016. Learning 3d deformation of animals from 2d images. In Computer Graphics Forum, Vol. 35. Wiley Online Library, 365--374.

[27]

Angjoo Kanazawa, Shubham Tulsiani, Alexei A. Efros, and Jitendra Malik. 2018. Learning Category-Specific Mesh Reconstruction from Image Collections. In ECCV.

[28]

Ladislav Kavan, Steven Collins, Jiří Žára, and Carol O'Sullivan. 2008. Geometric skinning with approximate dual quaternion blending. ACM Transactions on Graphics (TOG) 27, 4 (2008), 1--23.

Digital Library

[29]

Theodore Kim and David Eberle. 2020. Dynamic Deformables: Implementation and Production Practicalities. In ACM SIGGRAPH 2020 Courses (Virtual Event, USA) (SIGGRAPH '20). Association for Computing Machinery, New York, NY, USA, Article 23, 182 pages.

Digital Library

[30]

Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980

[31]

Shahar Z Kovalsky, Noam Aigerman, Ronen Basri, and Yaron Lipman. 2014. Controlling singular values with semidefinite programming. ACM Trans. Graph. 33, 4 (2014), 68--1.

Digital Library

[32]

Bruno Lévy, Sylvain Petitjean, Nicolas Ray, and Jérôme Maillot. 2002. Least Squares Conformal Maps for Automatic Texture Atlas Generation. In SIGGRAPH.

[33]

Peizhuo Li, Kfir Aberman, Rana Hanocka, Libin Liu, Olga Sorkine-Hornung, and Baoquan Chen. 2021. Learning Skeletal Articulations with Neural Blend Shapes. ACM Transactions on Graphics (TOG) 40, 4 (2021), 1.

Digital Library

[34]

Yaron Lipman. 2012. Bounded distortion mapping spaces for triangular meshes. ACM Transactions on Graphics (TOG) 31, 4 (2012), 1--13.

Digital Library

[35]

Yaron Lipman, David Levin, and Daniel Cohen-Or. 2008. Green coordinates. ACM Transactions on Graphics (TOG) 27, 3 (2008), 1--10.

Digital Library

[36]

Yaron Lipman, Olga Sorkine, Daniel Cohen-Or, David Levin, Christian Rossi, and Hans-Peter Seidel. 2004. Differential coordinates for interactive mesh editing. In Proceedings Shape Modeling Applications, 2004. IEEE, 181--190.

[37]

Or Litany, Alex Bronstein, Michael Bronstein, and Ameesh Makadia. 2018. Deformable shape completion with graph convolutional autoencoders. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1886--1895.

[38]

Ligang Liu, Lei Zhang, Yin Xu, Craig Gotsman, and Steven J. Gortler. 2008a. A Local/Global Approach to Mesh Parameterization. In Proceedings of the Symposium on Geometry Processing (Copenhagen, Denmark) (SGP '08). Eurographics Association, Goslar, DEU, 1495--1504.

[39]

Ligang Liu, Lei Zhang, Yin Xu, Craig Gotsman, and Steven J Gortler. 2008b. A local/global approach to mesh parameterization. In Computer Graphics Forum, Vol. 27. Wiley Online Library, 1495--1504.

Digital Library

[40]

Lijuan Liu, Youyi Zheng, Di Tang, Yi Yuan, Changjie Fan, and Kun Zhou. 2019. NeuroSkinning: Automatic skin binding for production characters with deep graph networks. ACM Transactions on Graphics (TOG) 38, 4 (2019), 1--12.

Digital Library

[41]

Minghua Liu, Minhyuk Sung, Radomir Mech, and Hao Su. 2021. DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12--21.

[42]

Luca Morreale, Noam Aigerman, Vladimir G Kim, and Niloy J Mitra. 2021. Neural Surface Maps. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4639--4648.

[43]

Ashish Myles and Denis Zorin. 2013. Controlled-distortion constrained global parametrization. ACM Transactions on Graphics (TOG) 32, 4 (2013), 1--14.

Digital Library

[44]

Ryosuke Okuta, Yuya Unno, Daisuke Nishino, Shohei Hido, and Crissman Loomis. 2017. CuPy: A NumPy-Compatible Library for NVIDIA GPU Calculations. In Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS). http://learningsys.org/nips17/assets/papers/paper_16.pdf

[45]

Ahmed A A Osman, Timo Bolkart, and Michael J. Black. 2020. STAR: A Sparse Trained Articulated Human Body Regressor. In European Conference on Computer Vision (ECCV). 598--613. https://star.is.tue.mpg.de

[46]

Jeong Joon Park, Peter Florence, Julian Straub, Richard A. Newcombe, and Steven Lovegrove. 2019. DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. CVPR (2019).

[47]

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Eds.). Curran Associates, Inc., 8024--8035. http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf

Digital Library

[48]

Ulrich Pinkall and Konrad Polthier. 1993. Computing Discrete Minimal Surfaces and Their Conjugates. EXPERIMENTAL MATHEMATICS 2 (1993), 15--36.

[49]

Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. 2017. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 652--660.

[50]

Michael Rabinovich, Roi Poranne, Daniele Panozzo, and Olga Sorkine-Hornung. 2017. Scalable Locally Injective Mappings. ACM Transactions on Graphics 36, 2 (April 2017), 16:1--16:16.

Digital Library

[51]

Cristian Romero, Dan Casas, Jesus Perez, and Miguel A. Otaduy. 2021. Learning Contact Corrections for Handle-Based Subspace Dynamics. ACM Trans. on Graphics (Proc. of ACM SIGGRAPH) 40, 4 (2021). http://gmrv.es/Publications/2021/RCPO21

[52]

Yusuf Sahillioğlu. 2020. Recent advances in shape correspondence. The Visual Computer 36, 8 (2020), 1705--1721.

Digital Library

[53]

Christian Schüller, Ladislav Kavan, Daniele Panozzo, and Olga Sorkine-Hornung. 2013. Locally injective mappings. In Computer Graphics Forum, Vol. 32. Wiley Online Library, 125--135.

[54]

Nicholas Sharp, Souhaib Attaiki, Keenan Crane, and Maks Ovsjanikov. 2022. Diffusion-net: Discretization agnostic learning on surfaces. ACM Transactions on Graphics (TOG) 41, 3 (2022), 1--16.

Digital Library

[55]

Alla Sheffer, K Hormann, B Levy, M Desbrun, K Zhou, E Praun, and H Hoppe. 2007. Mesh parameterization: Theory and practice. ACM SIGGRAPPH, course notes 10, 1281500.1281510 (2007).

[56]

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 Transactions on Graphics.

[57]

Jason Smith and Scott Schaefer. 2015. Bijective Parameterization with Free Boundaries. ACM Trans. Graph. 34, 4, Article 70 (jul 2015), 9 pages.

Digital Library

[58]

Olga Sorkine and Marc Alexa. 2007. As-Rigid-As-Possible Surface Modeling. In SGP.

[59]

Olga Sorkine and Mario Botsch. 2009. Interactive Shape Modeling and Deformation. In EUROGRAPHICS Tutorials.

[60]

Olga Sorkine, Daniel Cohen-Or, Yaron Lipman, Marc Alexa, Christian Rössl, and H-P Seidel. 2004. Laplacian surface editing. In Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing. 175--184.

Digital Library

[61]

Robert W Sumner and Jovan Popović. 2004. Deformation transfer for triangle meshes. ACM Transactions on graphics (TOG) 23, 3 (2004), 399--405.

[62]

Bo Sun, Xiangru Huang, Qixing Huang, Zaiwei Zhang, Junfeng Jiang, and Chandrajit Bajaj. 2021. ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators. In ICCV.

[63]

Qingyang Tan, Lin Gao, Yu-Kun Lai, and Shihong Xia. 2018. Variational Autoencoders for Deforming 3D Mesh Models. In CVPR.

[64]

Marco Tarini, Kai Hormann, Paolo Cignoni, and Claudio Montani. 2004. PolyCube-Maps. In ACM SIGGRAPH 2004 Papers (Los Angeles, California) (SIGGRAPH '04). Association for Computing Machinery, New York, NY, USA, 853--860.

Digital Library

[65]

Mikaela Angelina Uy, Jingwei Huang, Minhyuk Sung, Tolga Birdal, and Leonidas Guibas. 2020. Deformation-aware 3d model embedding and retrieval. In European Conference on Computer Vision. Springer, 397--413.

Digital Library

[66]

Gül Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J. Black, Ivan Laptev, and Cordelia Schmid. 2017. Learning from Synthetic Humans. In CVPR.

[67]

Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang. 2018. Pixel2mesh: Generating 3d mesh models from single rgb images. In Proceedings of the European Conference on Computer Vision (ECCV). 52--67.

Digital Library

[68]

Ofir Weber and Denis Zorin. 2014. Locally injective parametrization with arbitrary fixed boundaries. ACM Transactions on Graphics (TOG) 33, 4 (2014), 1--12.

Digital Library

[69]

Francis Williams, Teseo Schneider, Claudio Silva, Denis Zorin, Joan Bruna, and Daniele Panozzo. 2019. Deep geometric prior for surface reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10130--10139.

[70]

Yuxin Wu and Kaiming He. 2018. Group normalization. In Proceedings of the European conference on computer vision (ECCV). 3--19.

Digital Library

[71]

Zhan Xu, Yang Zhou, Evangelos Kalogerakis, Chris Landreth, and Karan Singh. 2020. RigNet: Neural Rigging for Articulated Characters. ACM Trans. on Graphics 39 (2020).

Digital Library

[72]

Zhan Xu, Yang Zhou, Evangelos Kalogerakis, and Karan Singh. 2019. Predicting Animation Skeletons for 3D Articulated Models via Volumetric Nets. In 2019 International Conference on 3D Vision (3DV).

[73]

Guandao Yang, Serge Belongie, Bharath Hariharan, and Vladlen Koltun. 2021. Geometry Processing with Neural Fields. NeurIPS.

[74]

Wang Yifan, Noam Aigerman, Vladimir G. Kim, Siddhartha Chaudhuri, and Olga Sorkine-Hornung. 2020. Neural Cages for Detail-Preserving 3D Deformations. In CVPR.

[75]

Kangxue Yin, Jun Gao, Maria Shugrina, Sameh Khamis, and Sanja Fidler. 2021. 3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations. In Proceedings of International Conference on Computer Vision (ICCV).

[76]

Yizhou Yu, Kun Zhou, Dong Xu, Xiaohan Shi, Hujun Bao, Baining Guo, and Heung-Yeung Shum. 2004. Mesh editing with poisson-based gradient field manipulation. In ACM SIGGRAPH 2004 Papers. 644--651.

Digital Library

[77]

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. 5932--5940.

[78]

Qingnan Zhou and Alec Jacobson. 2016. Thingi10k: A dataset of 10,000 3d-printing models. arXiv preprint arXiv:1605.04797 (2016).

[79]

Silvia Zuffi, Angjoo Kanazawa, David Jacobs, and Michael J. Black. 2017. 3D Menagerie: Modeling the 3D Shape and Pose of Animals. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR).

Cited By

Yu ZJin QWang HNi BZhang W(2025)HybriDeformer: A hybrid deformation method for arbitrary 3D avatar controllingDisplays10.1016/j.displa.2024.10293687(102936)Online publication date: Apr-2025
https://doi.org/10.1016/j.displa.2024.102936
Sundararaman RDonati NMelzi SCorman EOvsjanikov M(2024)Deformation Recovery: Localized Learning for Detail-Preserving DeformationsACM Transactions on Graphics10.1145/368796843:6(1-16)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687968
Xiu YYe YLiu ZTzionas DBlack M(2024)PuzzleAvatar: Assembling 3D Avatars from Personal AlbumsACM Transactions on Graphics10.1145/368777143:6(1-15)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687771
Show More Cited By

Index Terms

Neural jacobian fields: learning intrinsic mappings of arbitrary meshes
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
      1. Mesh geometry models
      2. Mesh models
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Recommendations

Bijective maps from simplicial foliations

This paper presents a method for bijective parametrization of 2D and 3D objects over canonical domains. While a range of solutions for the two-dimensional case are well-known, our method guarantees bijectivity of mappings also for a large, ...
Pose Space Deformation: A Unified Approach to Shape Interpolation and Skeleton-Driven Deformation
Seminal Graphics Papers: Pushing the Boundaries, Volume 2

Pose space deformation generalizes and improves upon both shape interpolation and common skeleton-driven deformation techniques. This deformation approach proceeds from the observation that several types of deformation can be uniformly represented as ...
Pose space deformation: a unified approach to shape interpolation and skeleton-driven deformation
SIGGRAPH '00: Proceedings of the 27th annual conference on Computer graphics and interactive techniques

Pose space deformation generalizes and improves upon both shape interpolation and common skeleton-driven deformation techniques. This deformation approach proceeds from the observation that several types of deformation can be uniformly represented as ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 41, Issue 4

July 2022

1978 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3528223

Issue’s Table of Contents

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 July 2022

Published in TOG Volume 41, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

25
Total Citations
View Citations
562
Total Downloads

Downloads (Last 12 months)163
Downloads (Last 6 weeks)5

Reflects downloads up to 27 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yu ZJin QWang HNi BZhang W(2025)HybriDeformer: A hybrid deformation method for arbitrary 3D avatar controllingDisplays10.1016/j.displa.2024.10293687(102936)Online publication date: Apr-2025
https://doi.org/10.1016/j.displa.2024.102936
Sundararaman RDonati NMelzi SCorman EOvsjanikov M(2024)Deformation Recovery: Localized Learning for Detail-Preserving DeformationsACM Transactions on Graphics10.1145/368796843:6(1-16)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687968
Xiu YYe YLiu ZTzionas DBlack M(2024)PuzzleAvatar: Assembling 3D Avatars from Personal AlbumsACM Transactions on Graphics10.1145/368777143:6(1-15)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687771
Liu TZhang TChen YHuang YWang C(2024)Neural Slicer for Multi-Axis 3D PrintingACM Transactions on Graphics10.1145/365821243:4(1-15)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3658212
Kusupati UGaillard MThiery JKaiser A(2024)Semantic Shape Editing with Parametric Implicit TemplatesACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657421(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657421
Li PWang TKesdogan TCeylan DSorkine‐Hornung O(2024)Neural Garment Dynamics via Manifold‐Aware TransformersComputer Graphics Forum10.1111/cgf.1502843:2Online publication date: 30-Apr-2024
https://doi.org/10.1111/cgf.15028
Sun BGroueix TSong CHuang QAigerman N(2024)TutteNet: Injective 3D Deformations by Composition of 2D Mesh Deformations2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02020(21378-21389)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02020
Dutt NMuralikrishnan SMitra N(2024)Diffusion 3D Features (Diff3F) Decorating Untextured Shapes with Distilled Semantic Features2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00430(4494-4504)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00430
Yoo SKim KKim VSung M(2024)As-Plausible-As-Possible: Plausibility-Aware Mesh Deformation Using 2D Diffusion Priors2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00413(4315-4324)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00413
Muralikrishnan SHuang CCeylan DMitra N(2024)BLiSS: Bootstrapped Linear Shape Space2024 International Conference on 3D Vision (3DV)10.1109/3DV62453.2024.00018(569-580)Online publication date: 18-Mar-2024
https://doi.org/10.1109/3DV62453.2024.00018
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents