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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Vydané v:	IEEE computer graphics and applications Ročník 37; číslo 4; s. 42 - 49
Hlavní autori:	Holden, Daniel, Habibie, Ikhsanul, Kusajima, Ikuo, Komura, Taku
Médium:	Magazine Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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