  Landmark/pose estimation in single monocular images have received much effort
in computer vision due to its important applications. It remains a challenging
task when input images severe occlusions caused by, e.g., adverse camera views.
Under such circumstances, biologically implausible pose predictions may be
produced. In contrast, human vision is able to predict poses by exploiting
geometric constraints of landmark point inter-connectivity. To address the
problem, by incorporating priors about the structure of pose components, we
propose a novel structure-aware fully convolutional network to implicitly take
such priors into account during training of the deep network. Explicit learning
of such constraints is typically challenging. Instead, inspired by how human
identifies implausible poses, we design discriminators to distinguish the real
poses from the fake ones (such as biologically implausible ones). If the pose
generator G generates results that the discriminator fails to distinguish from
real ones, the network successfully learns the priors. Training of the network
follows the strategy of conditional Generative Adversarial Networks (GANs). The
effectiveness of the proposed network is evaluated on three pose-related tasks:
2D single human pose estimation, 2D facial landmark estimation and 3D single
human pose estimation. The proposed approach significantly outperforms the
state-of-the-art methods and almost always generates plausible pose
predictions, demonstrating the usefulness of implicit learning of structures
using GANs.
