Hypergraph regularized autoencoder for image-based 3D human pose recovery

Image-based human pose recovery is usually conducted by retrieving relevant poses with image features. However, semantic gap exists for current feature extractors, which limits recovery performance. In this paper, we propose a novel feature extractor with deep learning. It is based on denoising autoencoder and improves traditional methods by adopting locality preserved restriction. To impose this restriction, we introduce manifold regularization with hypergraph Laplacian. Hypergraph Laplacian matrix is constructed with patch alignment framework. In this way, an automatic feature extractor for silhouettes is achieved. Experimental results on two datasets show that the recovery error has been reduced by 10% to 20%, which demonstrates the effectiveness of the proposed method. •Pose recovery with autoencoder is imposed locality reservation with Laplacian matrix.•The construction of Laplacian matrix is improved by using hypergraph optimization.