3D face and motion estimation from sparse points using adaptive bracketed minimization
This paper presents a novel method for estimating camera motion and reconstructing human face from a video sequence. The coarse-to-fine method is applied via combining the concepts of Powells minimization with gradient descent. Sparse points defining the human face in every frame are tracked using the active appearance model. The case of occluded points, even for self-occlusion, does not pose a problem in the proposed method. Robustness in the presence of noise and 3D accuracy using this method is also demonstrated. Examples of face reconstruction using other methods including trifocal tensor, Powells minimization, and gradient descent are also compared to the proposed method. Experiments on both synthetic and real faces are presented and analyzed. Also, different camera movement paths are illustrated. All real-world experiments used an off-the-shelf digital camera carried by a human walking without using any dolly to demonstrate the robustness and practicality of the proposed method.
gradient descent camera pose model reconstruction powells multidimensional minimization