  As a fundamental technique that concerns several vision tasks such as image
parsing, action recognition and clothing retrieval, human pose estimation (HPE)
has been extensively investigated in recent years. To achieve accurate and
reliable estimation of the human pose, it is well-recognized that the clothing
attributes are useful and should be utilized properly. Most previous
approaches, however, require to manually annotate the clothing attributes and
are therefore very costly. In this paper, we shall propose and explore a
\emph{latent} clothing attribute approach for HPE. Unlike previous approaches,
our approach models the clothing attributes as latent variables and thus
requires no explicit labeling for the clothing attributes. The inference of the
latent variables are accomplished by utilizing the framework of latent
structured support vector machines (LSSVM). We employ the strategy of
\emph{alternating direction} to train the LSSVM model: In each iteration, one
kind of variables (e.g., human pose or clothing attribute) are fixed and the
others are optimized. Our extensive experiments on two real-world benchmarks
show the state-of-the-art performance of our proposed approach.
