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Co-constrained handles for deformation in shape collections

Published: 19 November 2014 Publication History

Abstract

We present a method for learning custom deformation handles for an object, from a co-analysis of similar objects. Our approach identifies the geometric and spatial constraints among the different parts of an object, and makes this information available through abstract shape handles. These handles allow the user to prescribe arbitrary deformation directives including free-form surface deformations. However, only a subset of admissible deformations is enabled to the user as learned from the constraint space. Example applications are presented in shape editing, co-deformation and style transfer.

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Published In

cover image ACM Transactions on Graphics
ACM Transactions on Graphics  Volume 33, Issue 6
November 2014
704 pages
ISSN:0730-0301
EISSN:1557-7368
DOI:10.1145/2661229
Issue’s Table of Contents
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]

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Association for Computing Machinery

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Publication History

Published: 19 November 2014
Published in TOG Volume 33, Issue 6

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Author Tags

  1. 3D shape deformation
  2. co-constraints
  3. co-deformation
  4. constraint learning
  5. shape sets
  6. style transfer

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Cited By

View all
  • (2023)Multiscale Mesh Deformation Component Analysis With Attention-Based AutoencodersIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.311252629:2(1301-1317)Online publication date: 1-Feb-2023
  • (2023)Analyzing part functionality via multi-modal latent space embedding and interweavingComputers & Graphics10.1016/j.cag.2023.06.031115(1-12)Online publication date: Oct-2023
  • (2022)GLASS: Geometric Latent Augmentation for Shape Spaces2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52688.2022.01800(470-479)Online publication date: Jun-2022
  • (2021)A Revisit of Shape Editing Techniques: From the Geometric to the Neural ViewpointJournal of Computer Science and Technology10.1007/s11390-021-1414-936:3(520-554)Online publication date: 31-May-2021
  • (2020)DeformSyncNetACM Transactions on Graphics10.1145/3414685.341778339:6(1-16)Online publication date: 27-Nov-2020
  • (2020)Interactive Sculpting of Digital Faces Using an Anatomical Modeling ParadigmComputer Graphics Forum10.1111/cgf.1407139:5(93-102)Online publication date: 12-Aug-2020
  • (2020)Co-skeletons: Consistent curve skeletons for shape familiesComputers & Graphics10.1016/j.cag.2020.05.006Online publication date: May-2020
  • (2019)DeepStyle: Multimodal Search Engine for Fashion and Interior DesignIEEE Access10.1109/ACCESS.2019.29235527(84613-84628)Online publication date: 2019
  • (2019)Learning Semantic Abstraction of Shape via 3D Region of InterestGraphical Models10.1016/j.gmod.2019.101038(101038)Online publication date: Jul-2019
  • (2018)Functionality Representations and Applications for Shape AnalysisComputer Graphics Forum10.1111/cgf.1338537:2(603-624)Online publication date: 22-May-2018
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