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- Joo, Kyungdon
- Robotics and Visual Intelligence Lab.
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| DC Field | Value | Language |
|---|---|---|
| dc.citation.conferencePlace | KO | - |
| dc.citation.title | 대한전자공학회 2021년도 하계종합학술대회 | - |
| dc.contributor.author | 김유왕 | - |
| dc.contributor.author | 김지연 | - |
| dc.contributor.author | 주경돈 | - |
| dc.contributor.author | 오태현 | - |
| dc.date.accessioned | 2024-01-31T21:39:05Z | - |
| dc.date.available | 2024-01-31T21:39:05Z | - |
| dc.date.created | 2022-01-07 | - |
| dc.date.issued | 2021-07-02 | - |
| dc.description.abstract | Reconstructing the 3D shape and pose of humans and animals is essential in many future applications, such as autonomous vehicle’s Forward Collision Avoidance Assist (FCA) algorithms. In this work, we leverage well designed low dimensional linear mesh models of human and animal, SMPL and SMAL, to jointly regress the 3D mesh for a single input RGB image. We show that our joint regression network can learn anatomical similarities among humans and other various animal species. | - |
| dc.identifier.bibliographicCitation | 대한전자공학회 2021년도 하계종합학술대회 | - |
| dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/77209 | - |
| dc.identifier.url | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE10591533 | - |
| dc.publisher | 대한전자공학회 | - |
| dc.title.alternative | Unified Pose and Shape Model for 3D Human-Animal Reconstruction | - |
| dc.title | 사람-동물 3차원 자세 및 형상 추정을 위한 단일 통합 모델 | - |
| dc.type | Conference Paper | - |
| dc.date.conferenceDate | 2021-06-30 | - |
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