During the past few years we have witnessed the development of many methodologies for building and fitting Statistical Deformable Models (SDMs). The construction of accurate SDMs requires careful annotation of images with regards to a consistent set of landmarks. However, the manual annotation of a large amount of images is a tedious, laborious and expensive procedure. Furthermore, for several deformable objects, e.g. human body, it is difficult to define a consistent set of landmarks, and, thus, it becomes impossible to train humans in order to accurately annotate a collection of images. Nevertheless, for the majority of objects, it is possible to extract the shape by object segmentation or even by shape drawing.
We show for the first time, to the best of our knowledge, that it is possible to construct SDMs by putting object shapes in dense correspondence. Such SDMs can be built with much less effort for a large battery of objects. Additionally, we show that, by sampling the dense model, a part-based SDM can be learned with its parts being in correspondence. We employ our framework to develop SDMs of human arms and legs, which can be used for the segmentation of the outline of the human body, as well as to provide better and more consistent annotations for body joints.
Annotation Corrections for FLIC and MPII can be found below:
In order to build dense correspondences between different shape instances of the same object class, we jointly estimate the optical flow among all the instances by imposing low-rank constrains, an approach that we call Shape Flow. Multiframe optical flow has originally been applied on video sequences, relying on the assumptions of colour consistency and motion smoothness. However, these assumptions do not hold in our case, where we have a collection of shapes. Therefore, we introduce appropriate modifications based on the consistency of image-based shape representation, as well as low-rank priors.
Additionally, we show that the proposed methodology can be applied on landmark localisation, even though it is not tailored for that task, achieving particularly good performance.
Figure 2: Cumulative error distributions over skeleton landmarks on BBC Pose database for the experiment.
The experiment demonstrates that it is feasible to use the proposed arm model in order to correct the annotations provided by current datasets. As mentioned above there are inconsistencies in the annotations of MPII , Fashion Pose  and FLIC . Due to the large variance in arm pose, it is difficult even for trained annotators to obtain consistent annotations between them.
By applying our outline patch-based AAM on the aforementioned databases, we managed to greatly correct the currently available annotations of the arm. Figure 3 shows indicative examples of the corrected landmarks. There is no doubt that points after correction demonstrate more consistency among images.
For detailed information please refer to paper here.
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