Fast Neural Style Transfer for Motion Data

Automating motion style transfer can help save animators time by allowing them to produce a single set of motions, which can then be automatically adapted for use with different characters. The proposed fast, efficient technique for performing neural style transfer of human motion data uses a feed-f...

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Veröffentlicht in:IEEE computer graphics and applications Jg. 37; H. 4; S. 42 - 49
Hauptverfasser: Holden, Daniel, Habibie, Ikhsanul, Kusajima, Ikuo, Komura, Taku
Format: Magazine Article
Sprache:Englisch
Veröffentlicht: United States IEEE 2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Analytical models
Animation
computer graphics
Data models
Data transfer (computers)
deep learning
Human motion
machine learning
motion capture
Motion control
Neural networks
Optimization
style transfer
Training
Virtual reality
ISSN:0272-1716, 1558-1756, 1558-1756
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Zusammenfassung:Automating motion style transfer can help save animators time by allowing them to produce a single set of motions, which can then be automatically adapted for use with different characters. The proposed fast, efficient technique for performing neural style transfer of human motion data uses a feed-forward neural network trained on a large motion database. The proposed framework can transform the style of motion thousands of times faster than previous approaches that use optimization.
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ISSN:0272-1716
1558-1756
1558-1756
DOI:10.1109/MCG.2017.3271464