Learning semantic deformation flows with 3D convolutional networks

M. Ersin Yumer*, Niloy J. Mitra

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

30 Scopus citations

Abstract

Shape deformation requires expert user manipulation even when the object under consideration is in a high fidelity format such as a 3D mesh. It becomes even more complicated if the data is represented as a point set or a depth scan with significant self occlusions. We introduce an end-to-end solution to this tedious process using a volumetric Convolutional Neural Network (CNN) that learns deformation flows in 3D. Our network architectures take the voxelized representation of the shape and a semantic deformation intention (e.g., make more sporty) as input and generate a deformation flow at the output. We show that such deformation flows can be trivially applied to the input shape, resulting in a novel deformed version of the input without losing detail information. Our experiments show that the CNN approach achieves comparable results with state of the art methods when applied to CAD models.When applied to single frame depth scans, and partial/noisy CAD models we achieve ∼60% less error compared to the state-of-the-art.

Original languageEnglish (US)
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
EditorsBastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
PublisherSpringer Verlag
Pages294-311
Number of pages18
ISBN (Print)9783319464657
DOIs
StatePublished - 2016
Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
Duration: Oct 8 2016Oct 16 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9910 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th European Conference on Computer Vision, ECCV 2016
Country/TerritoryNetherlands
CityAmsterdam
Period10/8/1610/16/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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