Multi-resolution 3D Morphable Models and Its Matching Method

Bong-Nam Kang, Heran Byun, Daijin Kim

Abstract: The inverse compositional image alignment (ICIA) is known as an efficient matching method for 3D morphable models (3DMMs). However, it requires a long computation time since the 3D face models consist of a large number of vertices. Also, it requires to recompute the Hessian matrix using the visible vertices every iteration. For a fast and an efficient matching, we propose the efficient and accurate hierarchical ICIA (HICIA) matching method for 3DMMs. The proposed matching method requires multi-resolution 3D face models and the Gaussian image pyramid. The multi-resolution 3D face models are built by sub-sampling at the 2:1 sampling rate to construct the lower-resolution 3D face models. For more accurate matching, we use a twostage model parameter update that only updates the rigid and the texture parameters and then updates all parameters after the initial convergence. We present several experimental results to prove that the proposed method shows better performance than that of the conventional ICIA matching method.