research-article

Modeling and generating moving trees from video

Authors:

Oliver Deussen,

Peter HallAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 30, Issue 6

Pages 1 - 12

https://doi.org/10.1145/2070781.2024161

Published: 12 December 2011 Publication History

Abstract

We present a probabilistic approach for the automatic production of tree models with convincing 3D appearance and motion. The only input is a video of a moving tree that provides us an initial dynamic tree model, which is used to generate new individual trees of the same type. Our approach combines global and local constraints to construct a dynamic 3D tree model from a 2D skeleton. Our modeling takes into account factors such as the shape of branches, the overall shape of the tree, and physically plausible motion. Furthermore, we provide a generative model that creates multiple trees in 3D, given a single example model. This means that users no longer have to make each tree individually, or specify rules to make new trees. Results with different species are presented and compared to both reference input data and state of the art alternatives.

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Cited By

Li BSchwarz NPałubicki WPirk SBenes B(2024)Interactive Invigoration: Volumetric Modeling of Trees with StrandsACM Transactions on Graphics10.1145/365820643:4(1-13)Online publication date: 19-Jul-2024
https://doi.org/10.1145/3658206
Peytavie AGain JGuérin EArgudo OGalin E(2024)DeadWood: Including Disturbance and Decay in the Depiction of Digital NatureACM Transactions on Graphics10.1145/364181643:2(1-19)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1145/3641816
Zhou XLi BBenes BFei SPirk S(2024)DeepTree: Modeling Trees With Situated LatentsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.330788730:8(5795-5809)Online publication date: Aug-2024
https://doi.org/10.1109/TVCG.2023.3307887
Show More Cited By

Index Terms

Modeling and generating moving trees from video
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
  2. Computer graphics
    1. Image manipulation
    2. Shape modeling
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Computational geometry

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 30, Issue 6

December 2011

678 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2070781

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Copyright © 2011 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 December 2011

Published in TOG Volume 30, Issue 6

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Cited By

Li BSchwarz NPałubicki WPirk SBenes B(2024)Interactive Invigoration: Volumetric Modeling of Trees with StrandsACM Transactions on Graphics10.1145/365820643:4(1-13)Online publication date: 19-Jul-2024
https://doi.org/10.1145/3658206
Peytavie AGain JGuérin EArgudo OGalin E(2024)DeadWood: Including Disturbance and Decay in the Depiction of Digital NatureACM Transactions on Graphics10.1145/364181643:2(1-19)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1145/3641816
Zhou XLi BBenes BFei SPirk S(2024)DeepTree: Modeling Trees With Situated LatentsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.330788730:8(5795-5809)Online publication date: Aug-2024
https://doi.org/10.1109/TVCG.2023.3307887
Selvaraju P(2024)Developability Approximation for Neural Implicits Through Rank Minimization2024 International Conference on 3D Vision (3DV)10.1109/3DV62453.2024.00041(780-789)Online publication date: 18-Mar-2024
https://doi.org/10.1109/3DV62453.2024.00041
Li BKlein JMichels DBenes BPirk SPałubicki W(2023)Rhizomorph: The Coordinated Function of Shoots and RootsACM Transactions on Graphics10.1145/359214542:4(1-16)Online publication date: 26-Jul-2023
https://doi.org/10.1145/3592145
Cárdenas JOgayar CFeito FJurado J(2023)Modeling of the 3D Tree Skeleton Using Real-World Data: A SurveyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.319301829:12(4920-4935)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TVCG.2022.3193018
Han QJung C(2023)Cross Spectral Disparity Estimation From VIS and NIR Paired Images Using Disentangled Representation and Reversible Neural NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.323880024:5(5326-5336)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3238800
Ye YZhou MWang ZShen X(2023)Improved Upsampling Based Depth Image Super-Resolution ReconstructionIEEE Access10.1109/ACCESS.2023.327496611(46782-46792)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3274966
Liu XFu SLin BNie X(2023)Windowed variation kernel Wiener filter model for image denoising with edge preservationOptics & Laser Technology10.1016/j.optlastec.2023.109688167(109688)Online publication date: Dec-2023
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Fu BZhang XWang LRen YThanh D(2022)A blind medical image denoising method with noise generation networkJournal of X-Ray Science and Technology10.3233/XST-21109830:3(531-547)Online publication date: 15-Apr-2022
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