Publications by authors named "Ikuo Kusajima"

2 Publications

  • Page 1 of 1

Fast Neural Style Transfer for Motion Data.

IEEE Comput Graph Appl 2017 ;37(4):42-49

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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http://dx.doi.org/10.1109/MCG.2017.3271464DOI Listing
October 2018

Generating action descriptions from statistically integrated representations of human motions and sentences.

Neural Netw 2016 Aug 16;80:1-8. Epub 2016 Mar 16.

Mechano-Informatics, University of Tokyo, 7-3-1 Hongo, Bunkyoku, Tokyo, 113-8656, Japan. Electronic address:

It is desirable for robots to be able to linguistically understand human actions during human-robot interactions. Previous research has developed frameworks for encoding human full body motion into model parameters and for classifying motion into specific categories. For full understanding, the motion categories need to be connected to the natural language such that the robots can interpret human motions as linguistic expressions. This paper proposes a novel framework for integrating observation of human motion with that of natural language. This framework consists of two models; the first model statistically learns the relations between motions and their relevant words, and the second statistically learns sentence structures as word n-grams. Integration of these two models allows robots to generate sentences from human motions by searching for words relevant to the motion using the first model and then arranging these words in appropriate order using the second model. This allows making sentences that are the most likely to be generated from the motion. The proposed framework was tested on human full body motion measured by an optical motion capture system. In this, descriptive sentences were manually attached to the motions, and the validity of the system was demonstrated.
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http://dx.doi.org/10.1016/j.neunet.2016.03.001DOI Listing
August 2016