Image-based 3D Human Pose Recovery with Locality Sensitive Sparse Retrieval

Image-based 3D human pose recovery is usually conducted by retrieving relevant poses with image features. However, it suffers from high dimensionality of image features and low efficiency of retrieving process. In this paper, we propose a novel approach to recover 3D human poses from silhouettes. This approach improves traditional methods by adopting locality sensitive sparse coding in the retrieving process. It incorporates a local similarity preserving term into the objective of sparse coding, which groups similar silhouettes to alleviate the instability of sparse codes. The experimental results demonstrate the effectiveness of the proposed method.