Human pose estimation via convolutional part heatmap regression
- Submitting institution
-
Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 532
- Type
- E - Conference contribution
- DOI
-
10.1007/978-3-319-46478-7_44
- Title of conference / published proceedings
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- First page
- 717
- Volume
- 9911 LNCS
- Issue
- -
- ISSN
- 0302-9743
- Open access status
- Technical exception
- Month of publication
- September
- Year of publication
- 2016
- URL
-
-
- 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
-
1
- Research group(s)
-
-
- Citation count
- 133
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "This highly cited paper provided the main technology for the University of Nottingham submission which won the 1st place (out of 8 submissions) in the 3D Face Alignment in-the-Wild (3DFAW) Challenge, held in conjunction with ECCV 2016. The method from the paper was used as is (trained on the challenge provided facial data) to estimate the 2D (X,Y) facial landmark coordinates. The only difference between the submission and the original paper was the inclusion of another network to estimate the Z (depth) coordinate. See http://mhug.disi.unitn.it/workshop/3dfaw/ and
https://competitions.codalab.org/competitions/10261#results."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -