  Understanding humans from photographs has always been a fundamental goal of
computer vision. In this thesis we have developed a hierarchy of tools that
cover a wide range of topics with the objective of understanding humans from
monocular RGB image: from low level feature point descriptors to high level
fashion-aware conditional random fields models. In order to build these high
level models it is paramount to have a battery of robust and reliable low and
mid level cues. Along these lines, we have proposed two low-level keypoint
descriptors: one based on the theory of the heat diffusion on images, and the
other that uses a convolutional neural network to learn discriminative image
patch representations. We also introduce distinct low-level generative models
for representing human pose: in particular we present a discrete model based on
a directed acyclic graph and a continuous model that consists of poses
clustered on a Riemannian manifold. As mid level cues we propose two 3D human
pose estimation algorithms: one that estimates the 3D pose given a noisy 2D
estimation, and an approach that simultaneously estimates both the 2D and 3D
pose. Finally, we formulate higher level models built upon low and mid level
cues for understanding humans from single images. Concretely, we focus on two
different tasks in the context of fashion: semantic segmentation of clothing,
and predicting the fashionability from images with metadata to ultimately
provide fashion advice to the user. For all presented approaches we present
extensive results and comparisons against the state-of-the-art and show
significant improvements on the entire variety of tasks we tackle.
