Three-dimensional image-based human pose recovery with hypergraph regularized autoencoders

Three-Dimensional image-based human pose recovery tries to retrieves 3D poses with 2D image. Therefore, one of the key problem is how to represent 2D images. However, semantic gap exists for current feature extractors, which limits recovery performance. In this paper, we propose a novel feature extractor with deep neural network. It is based on denoising autoencoders and improves previous autoencoders by adopting locality preserved restriction. To impose this restriction, we introduce manifold regularization with hypergraph learning. Hypergraph Laplacian matrix is constructed with patch alignment framework. In this way, an automatic feature extractor for images is achieved. Experimental results on three datasets show that the recovery error can be reduced by 10 % to 20 %, which demonstrates the effectiveness of the proposed method.