Expression-invariant and sparse representation for mesh-based compression for 3-D face models

Compression of mesh-based 3-D models has been an important issue, which ensures efficient storage and transmission. In this paper, we present a very effective compression scheme specifically for expression variation 3-D face models. Firstly, 3-D models are mapped into 2-D parametric domain and corresponded by expression-invariant parameterizaton, leading to 2-D image format representation namely geometry images, which simplifies the 3-D model compression into 2-D image compression. Then, sparse representation with learned dictionaries via K-SVD is applied to each patch from sliced GI so that only few coefficients and their indices are needed to be encoded, leading to low datasize. Experimental results demonstrate that the proposed scheme provides significant improvement in terms of compression performance, especially at low bitrate, compared with existing algorithms.