Fast Neural Style Transfer for Motion Data - Holden, Daniel; Habibie, Ikhsanul; Kusajima, Ikuo; Komura, Taku (IEEE computer graphics and applications, 2017)
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.