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We propose a factorization structure from motion (SfM) framework which employs 3D active shape constraints for a 3D face model
application. Two types of shape model, individual shape models and a generic model, are used to approximate non-linear manifold
variation. When the 3D shape models are trained, they help the SfM algorithm to reconstruct the...
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... to [5,8], rotation constraints are sufficient to reconstruct 3D structural infor- mation in the case of a rigid-shape object, with rigid motion when the correspondences of the salient points are tracked correctly. Thus the 2D tracking points are labeled man- ually before building the 3D shape models; the 68 salient points, defined in [11], are used to track the face, shown in Figure 2. Two forms of ambiguity still exist when the 2D observations are fed into the SfM framework to recover the 3D shape. ...
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Citations
... The algorithm was used to track two faces in two downsampled sequences (every 10 th frame, 60 and 50 frames for individuals respectively), which are known to yield several singular columns if the SfM algorithm is used to recover the 3D shape via rank constraints. One sequence was from FG-NET [10] and the other, shorter sequence is from a new data-set [12] . These sequences had previously been manually annotated with a consistent set of 68 points. ...
... The rotation constraints alone are not enough to approach the correct solution in most automatic tracking cases. Shape constraints can be added via a generic shape model [12] face drawn from the normalized faces using the Delauney triangulation built from the tracked sequence was mapped onto the 3D shape. The textured face is shown in figure 6.Figure 6. ...
We describe an algorithm for automatically finding correspondences from face video sequences. This method is useful to many applications such as face tracking, face modeling and 3D face recovery. Given a sequence of images, the face feature points are tracked by a model-constraint optical flow algorithm. By employing a minimum description length (MDL) point-refinement framework, the drift-off error caused by the optical flow algorithm can be reduced and the correspondences can be matched robustly by optimizing the statistical model. As a result, the face is able to be tracked precisely. Furthermore, it offers a new method of building an appearance model automatically. The objective root mean square error (RMSE) is used to prove the efficiency of the algorithm. At the same time, the performance is evaluated subjectively by generating 3D face models based upon it.
