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Real-Time Non-Rigid Shape Recovery via AAMs for Augmented Reality Jackie Zhu Oct. 24, 2006.

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Presentation on theme: "Real-Time Non-Rigid Shape Recovery via AAMs for Augmented Reality Jackie Zhu Oct. 24, 2006."— Presentation transcript:

1 Real-Time Non-Rigid Shape Recovery via AAMs for Augmented Reality Jackie Zhu Oct. 24, 2006

2 Outline Introduction Extended Active Appearance Models (AAMs) Fitting Algorithm Offline construction of 3D shape model Estimate 3D pose and non-rigid shape parameters Experiemental Results Conclusion

3 Backgroud Rigid Object L. Vacchetti et al. (PAMI‘04) proposed an efficient solution for 3D rigid object tracking Two 2D AAMs approach for rigid object pose estimation Non-rigid Object V.Blandz: 3D Morphable Models J.Ahlberg: 3D AAM with generic Model. Jing X. (CVPR‘05) 2D+3D AAM

4 Overview

5 Extended AAMs Fitting Algorithm

6 AAM Fitting Sample The AAMs are built up with 140 still face image belonging to 20 individuals, 7 images for each. The fitting experiment is performed on an AAM with 14 shape parameters, 68 texture parameters, and 36335 color pixels.

7 Algorithm Building offline basis: Acquire the 2D shape of objects using the AAM fitting algorithm, then construct the 3D shape basis. Online tracking: Estimate the 3D pose and shape parameters simultaneously via local bundle adjustment by building up the point correspondences between 2D and 3D.

8 Algorithm: Offline 3D Model

9 Algorithm: Online Pose Estimation The optimization problem can be derived as: Where

10 Experiemental Results I

11 Experimental Results II

12 Experimental Results

13 Conclusion A novel two-stage scheme for online non-rigid shape recovery toward Augmented Reality applications using AAMs. Obtain unbroken point correspondences across multiple frames to construct 3D shape models Provide 2D to 3D vertex correspondences in the online tracking. An efficient algorithm is proposed to estimate both 3D pose and non-rigid shape parameters via local bundle adjustment.

14 References: Ahlberg, J.: Using the active appearance algorithm for face and facial feature tracking. In: Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 2001. Proceedings. IEEE ICCV Workshop on. (2001) 68-72 Xiao, J., Baker, S., Matthews, I., Kanade, T.: Real-time combined 2d+3d active appearance models. In: IEEE CVPR'2004. Volume 2. (2004) 535-542 Vacchetti, L., Lepetit, V., Fua, P.: Stable real-time 3d tracking using online and offline information. IEEE Trans. PAMI 26 (2004) Cootes, T., Edwards, G., Taylo, C.: Active appearance models. IEEE Trans. PAMI 23 (2001) C.Bregler, A.Hertzmann, H.Biermann: Recovering non-rigid 3d shape from image streams. In: IEEE CVPR'2000. Volume 2. (2000) 690-696 Jianke Zhu, Steven C.H. Hoi and Michael R. Lyu: The Real-Time Non-Rigid Shape Recovery via Active Appearance Models for Augmented Reality. In: ECCV'2006. Volume 1. LNCS 3951. (2006) 186-197.

15 The End Thank You !


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