Automatic unpaired shape deformation transfer
- Submitting institution
-
Cardiff University / Prifysgol Caerdydd
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 95727626
- Type
- D - Journal article
- DOI
-
10.1145/3272127.3275028
- Title of journal
- ACM Transactions on Graphics
- Article number
- 237
- First page
- -
- Volume
- 37
- Issue
- 6
- ISSN
- 0730-0301
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2018
- URL
-
https://doi.org/10.1145/3272127.3275028
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
6
- Research group(s)
-
V - Visual computing
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This collaboration with the Chinese Academy of Sciences and Zhejiang University proposes a novel deep generative architecture that, for the first time, achieves automatic unpaired shape deformation transfer. It was presented at ACM SIGGRAPH Asia 2018. The research was funded by a Royal Society Newton Mobility grant (IE150731) and forms the research basis that leads to a Royal Society Newton Advanced Fellowship award (NAF\R2\192151). The source code is publicly available for research purposes (https://github.com/gaolinorange/Automatic-Unpaired-Shape-Deformation-Transfer), and the paper received the ACM Reproducibility Badge and Graphics Replicability Stamp (http://www.replicabilitystamp.org).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -