Hypergraph regularized autoencoder for 3D human pose recovery

Abstract

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. Hyper- graph 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.