Fast Terrain-Adaptive Motion Generation using Deep Neural Networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yu | - |
dc.contributor.author | M. | - |
dc.contributor.author | Kwon | - |
dc.contributor.author | B. | - |
dc.contributor.author | Kim | - |
dc.contributor.author | J. | - |
dc.contributor.author | Kang, Shinjin | - |
dc.contributor.author | S. | - |
dc.contributor.author | Jang | - |
dc.contributor.author | H. | - |
dc.date.available | 2021-03-17T08:01:37Z | - |
dc.date.created | 2021-02-26 | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/12826 | - |
dc.description.abstract | We propose a fast motion adaptation framework using deep neural networks. Traditionally, motion adaptation is performed via iterative numerical optimization. We adopted deep neural networks and replaced the iterative process with the feed-forward inference consisting of simple matrix multiplications. For efficient mapping from contact constraints to character motion, the proposed system is composed of two types of networks: trajectory and pose generators. The networks are trained using augmented motion capture data and are fine-tuned using the inverse kinematics loss. In experiments, our system successfully generates multi-contact motions of a hundred of characters in real-time, and the result motions contain the naturalness existing in the motion capture data. | - |
dc.publisher | ASSOC COMPUTING MACHINERY | - |
dc.title | Fast Terrain-Adaptive Motion Generation using Deep Neural Networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kang, Shinjin | - |
dc.identifier.doi | 10.1145/3355088.3365157 | - |
dc.identifier.scopusid | 2-s2.0-85076705493 | - |
dc.identifier.wosid | 000535124100015 | - |
dc.identifier.bibliographicCitation | SIGGRAPH Asia 2019 Technical Briefs, SA 2019, pp.57 - 60 | - |
dc.relation.isPartOf | SIGGRAPH Asia 2019 Technical Briefs, SA 2019 | - |
dc.citation.title | SIGGRAPH Asia 2019 Technical Briefs, SA 2019 | - |
dc.citation.startPage | 57 | - |
dc.citation.endPage | 60 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.subject.keywordAuthor | Character Animation | - |
dc.subject.keywordAuthor | Inverse Kinematics | - |
dc.subject.keywordAuthor | Deep Neural Networks | - |
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