Automated 3D animation from snooker videos with information theoretic optimization

Automated 3D modeling from real sports videos can provide useful resources for visual design in sports-related computer games, saving a lot of efforts in manual design of visual contents. However, image-based 3D reconstruction usually suffers from inaccuracy caused by statistic image analysis. In this paper, we proposed an information theoretical scheme to minimize errors of automated 3D modeling from monocular sports videos. In the proposed scheme, mutual information was exploited to compute the fitting scores of a 3D 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 3D 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 3D space from single-view frames, and 3D 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 3D animation, and demonstrated that information theoretical evaluation can be an effective approach for model-based reconstruction from single-view videos.