research-article

Motion field texture synthesis

Authors:

Kun ZhouAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 28, Issue 5

Pages 1 - 8

https://doi.org/10.1145/1618452.1618456

Published: 01 December 2009 Publication History

Abstract

A variety of animation effects such as herds and fluids contain detailed motion fields characterized by repetitive structures. Such detailed motion fields are often visually important, but tedious to specify manually or expensive to simulate computationally. Due to the repetitive nature, some of these motion fields (e.g. turbulence in fluids) could be synthesized by procedural texturing, but procedural texturing is known for its limited generality.

We apply example-based texture synthesis for motion fields. Our technique is general and can take on a variety of user inputs, including captured data, manual art, and physical/procedural simulation. This data-driven approach enables artistic effects that are difficult to achieve via previous methods, such as heart shaped swirls in fluid animation. Due to the use of texture synthesis, our method is able to populate a large output field from a small input exemplar, imposing minimum user workload. Our algorithm also allows the synthesis of output motion fields not only with the same dimension as the input (e.g. 2D to 2D) but also of higher dimension, such as 3D volumetric outputs from 2D planar inputs. This cross-dimension capability supports a convenient usage scenario, i.e. the user could simply supply 2D images and our method produces a 3D motion field with similar characteristics. The motion fields produced by our method are generic, and could be combined with a variety of large-scale low-resolution motions that are easy to specify either manually or computationally but lack the repetitive structures to be characterized as textures. We apply our technique to a variety of animation phenomena, including smoke, liquid, and group motion.

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Cited By

Chu MThuerey NSeidel HTheobalt CZayer R(2021)Learning meaningful controls for fluidsACM Transactions on Graphics10.1145/3476576.347666140:4(1-13)Online publication date: Aug-2021
https://doi.org/10.1145/3476576.3476661
Chu MThuerey NSeidel HTheobalt CZayer R(2021)Learning meaningful controls for fluidsACM Transactions on Graphics10.1145/3450626.345984540:4(1-13)Online publication date: Aug-2021
https://doi.org/10.1145/3450626.3459845
Zhu QHan CHan GWong THe S(2021)Video Snapshot: Single Image Motion Expansion via Invertible Motion EmbeddingIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2020.300164443:12(4491-4504)Online publication date: 1-Dec-2021
https://doi.org/10.1109/TPAMI.2020.3001644
Show More Cited By

Index Terms

Motion field texture synthesis
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Shape representations
  2. Computer graphics
    1. Animation
    2. Image manipulation
      1. Texturing

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 28, Issue 5

December 2009

646 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1618452

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Copyright © 2009 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2009

Published in TOG Volume 28, Issue 5

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Cited By

Chu MThuerey NSeidel HTheobalt CZayer R(2021)Learning meaningful controls for fluidsACM Transactions on Graphics10.1145/3476576.347666140:4(1-13)Online publication date: Aug-2021
https://doi.org/10.1145/3476576.3476661
Chu MThuerey NSeidel HTheobalt CZayer R(2021)Learning meaningful controls for fluidsACM Transactions on Graphics10.1145/3450626.345984540:4(1-13)Online publication date: Aug-2021
https://doi.org/10.1145/3450626.3459845
Zhu QHan CHan GWong THe S(2021)Video Snapshot: Single Image Motion Expansion via Invertible Motion EmbeddingIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2020.300164443:12(4491-4504)Online publication date: 1-Dec-2021
https://doi.org/10.1109/TPAMI.2020.3001644
Wang PLiu LChen NChu HTheobalt CWang W(2020)Vid2CurveACM Transactions on Graphics10.1145/3386569.339247639:4(132:1-132:12)Online publication date: 12-Aug-2020
https://dl.acm.org/doi/10.1145/3386569.3392476
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Piovarči MKaufman DLevin DDidyk P(2020)Fabrication-in-the-loop co-optimization of surfaces and styli for drawing hapticsACM Transactions on Graphics10.1145/3386569.339246739:4(116:1-116:16)Online publication date: 12-Aug-2020
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Kotwal ALevin AGkioulekas I(2020)Interferometric transmission probing with coded mutual intensityACM Transactions on Graphics10.1145/3386569.339238439:4(74:1-74:16)Online publication date: 12-Aug-2020
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Zhang CMiller BYan KGkioulekas IZhao S(2020)Path-space differentiable renderingACM Transactions on Graphics10.1145/3386569.339238339:4(143:1-143:19)Online publication date: 12-Aug-2020
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Won JGopinath DHodgins J(2020)A scalable approach to control diverse behaviors for physically simulated charactersACM Transactions on Graphics10.1145/3386569.339238139:4(33:1-33:12)Online publication date: 12-Aug-2020
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Kim BAzevedo VGross MSolenthaler B(2019)Transport-based neural style transfer for smoke simulationsACM Transactions on Graphics10.1145/3355089.335656038:6(1-11)Online publication date: 8-Nov-2019
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