  We propose a novel cascaded framework, namely deep deformation network (DDN),
for localizing landmarks in non-rigid objects. The hallmarks of DDN are its
incorporation of geometric constraints within a convolutional neural network
(CNN) framework, ease and efficiency of training, as well as generality of
application. A novel shape basis network (SBN) forms the first stage of the
cascade, whereby landmarks are initialized by combining the benefits of CNN
features and a learned shape basis to reduce the complexity of the highly
nonlinear pose manifold. In the second stage, a point transformer network (PTN)
estimates local deformation parameterized as thin-plate spline transformation
for a finer refinement. Our framework does not incorporate either handcrafted
features or part connectivity, which enables an end-to-end shape prediction
pipeline during both training and testing. In contrast to prior cascaded
networks for landmark localization that learn a mapping from feature space to
landmark locations, we demonstrate that the regularization induced through
geometric priors in the DDN makes it easier to train, yet produces superior
results. The efficacy and generality of the architecture is demonstrated
through state-of-the-art performances on several benchmarks for multiple tasks
such as facial landmark localization, human body pose estimation and bird part
localization.
