Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Automated 3-D Animation From Snooker Videos With Information-Theoretical Optimization
AU - Jiang, Richard
AU - Parry, Matthew L.
AU - Legg, Phillip A.
AU - Chung, David H. S.
AU - Griffiths, Iwan W.
PY - 2013/12
Y1 - 2013/12
N2 - Automated 3-D modeling from real sports videos can provide useful resources for visual design in sports-related computer games, saving a lot of effort in manual design of visual contents. However, image-based 3-D reconstruction usually suffers from inaccuracy caused by statistic image analysis. In this paper, we propose an information-theoretical scheme to minimize errors of automated 3-D modeling from monocular sports videos. In the proposed scheme, mutual information (MI) was exploited to compute the fitting scores of a 3-D model against the observed single-view scene, and the optimization of model fitting was carried out subsequently. With this optimization scheme, errors in model fitting were minimized without human intervention, allowing automated reconstruction of 3-D animation from consecutive monocular video frames at high accuracy. In our work, the Snooker videos were taken as our case study, balls were positioned in 3-D space from single-view frames, and 3-D animation was reproduced from real Snooker videos. Our experimental results validated that the proposed information-theoretical scheme can help attain better accuracy in the automated reconstruction of 3-D animation, and demonstrated that information-theoretical evaluation can be an effective approach for model-based reconstruction from single-view videos.
AB - Automated 3-D modeling from real sports videos can provide useful resources for visual design in sports-related computer games, saving a lot of effort in manual design of visual contents. However, image-based 3-D reconstruction usually suffers from inaccuracy caused by statistic image analysis. In this paper, we propose an information-theoretical scheme to minimize errors of automated 3-D modeling from monocular sports videos. In the proposed scheme, mutual information (MI) was exploited to compute the fitting scores of a 3-D model against the observed single-view scene, and the optimization of model fitting was carried out subsequently. With this optimization scheme, errors in model fitting were minimized without human intervention, allowing automated reconstruction of 3-D animation from consecutive monocular video frames at high accuracy. In our work, the Snooker videos were taken as our case study, balls were positioned in 3-D space from single-view frames, and 3-D animation was reproduced from real Snooker videos. Our experimental results validated that the proposed information-theoretical scheme can help attain better accuracy in the automated reconstruction of 3-D animation, and demonstrated that information-theoretical evaluation can be an effective approach for model-based reconstruction from single-view videos.
KW - Automated 3-D modeling
KW - computer game design
KW - visual design
KW - 3-D animation
U2 - 10.1109/TCIAIG.2013.2275164
DO - 10.1109/TCIAIG.2013.2275164
M3 - Journal article
VL - 5
SP - 337
EP - 345
JO - IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES
JF - IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES
SN - 1943-068X
IS - 4
ER -