Output details
11 - Computer Science and Informatics
Lancaster University
Article title
Modeling spatial and temporal variation in motion data
Type
D - Journal article
Title of journal
ACM Transactions on Graphics
Article number
171
Volume number
28
Issue number
5
First page of article
-
ISSN of journal
0730-0301
Year of publication
2009
Number of additional authors
2
Additional information
<26> Proposes a fundamentally different approach to the problem of synthesising motion variation, featuring two important ideas: we are the first to take a data-driven approach to solve this problem, and the first to use Dynamic Bayesian Networks to model motion variation. Published in ACM Transactions on Graphics (world-leading journal in the area), and presented at the annual SIGGRAPH Asia conference (acceptance ratio: 70/275). Collaboration with faculty from Carnegie Mellon University. High citation rate is evidence for follow-up work. Using Bayesian Networks for human motion analysis has since led to significant research funding in the US by other researchers.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
18
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
