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Deep motion transfer without big data

Published: 12 August 2018 Publication History

Abstract

This paper presents a novel motion transfer algorithm that copies content motion into a specific style character. The input consists of two motions. One is a content motion such as walking or running, and the other is movement style such as zombie or Krall. The algorithm automatically generates the synthesized motion such as walking zombie, walking Krall, running zombie, or running Krall. In order to obtain natural results, the method adopts the generative power of deep neural networks. Compared to previous neural approaches, the proposed algorithm shows better quality, runs extremely fast, does not require big data, and supports user-controllable style weights.

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References

[1]
Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. 2016. Image Style Transfer using Convolutional Neural Networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2414--2423.
[2]
Kun He, Yan Wang, and John E. Hopcroft. 2016. A Powerful Generative Model using Random Weights for the Deep Image Representation. In Advances in Neural Information Processing Systems 29 (NIPS). 631--639.
[3]
Daniel Holden, Jun Saito, and Taku Komura. 2016. A Deep Learning Framework for Character Motion Synthesis and Editing. ACM Trans. Graph. 35 (2016), 138:1--138:11.

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cover image ACM Conferences
SIGGRAPH '18: ACM SIGGRAPH 2018 Posters
August 2018
148 pages
ISBN:9781450358170
DOI:10.1145/3230744
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2018

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Author Tags

  1. character animation synthesis
  2. deep neural networks

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Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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