Dense pose estimation of deformable objects
File(s)
Author(s)
Zhou, Yuxiang
Type
Thesis
Abstract
Statistical Deformable Models (SDMs) represent a widely used collection of computer vision techniques.
SDMs serve the purpose of modeling the deformation and variability of a specific class of objects, e.g.
faces, ears, hands, human poses, etc. SDMs have shown success in many areas of visual image analysis.
For example, in biometrics, SDMs are commonly used for recognition, classification, detection, and
alignment.
During the past few years, we have witnessed the development of many methodologies for building and
fitting Statistical Deformable Models. 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 number
of images is a tedious, laborious and expensive procedure. Furthermore, for several deformable objects,
e.g. the human body, it is difficult to define a consistent set of landmarks. Even though, the SDMs
construction still suffers in terms of sparse landmark annotation. With sparse annotations, the shape
deformation of any deformable objects is only captured where the key points are labeled, thus nuanced
shape deformation is discarded. It is merely possible to accurately annotate dense shape deforestation
manually.
Fortunately, for the majority of objects, it is possible to extract the shape by object segmentation or even
by shape drawing. In this thesis, we propose that it is possible to construct SDMs by putting objects
shapes in dense correspondence. Such SDMs can be built with much less effort for a large battery of
objects. Additionally, by sampling the dense model, a part-based SDM can be learned with its parts
being in correspondence. We propose a 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 articulated objects. Such a system can serve as an element of cascaded
architectures that jointly localize landmarks and estimate dense correspondences. Also, we show that the
obtained dense correspondence can act as a source of prior knowledge that complements and extends the
pure landmark-level annotations, accelerating and improving the training of pose estimation networks.
The thesis contributes towards fully automatic dense correspondence estimation. Such correspondence
would benefit any system that requires nuance shape deformation. We are approaching a milestone that
dense correspondence and poses for deformable objects can be estimated “in-the-wild” with minimal
effort on labeling. The estimated correspondence is shown to be useful for various downstream tasks for
a collection of objects.
SDMs serve the purpose of modeling the deformation and variability of a specific class of objects, e.g.
faces, ears, hands, human poses, etc. SDMs have shown success in many areas of visual image analysis.
For example, in biometrics, SDMs are commonly used for recognition, classification, detection, and
alignment.
During the past few years, we have witnessed the development of many methodologies for building and
fitting Statistical Deformable Models. 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 number
of images is a tedious, laborious and expensive procedure. Furthermore, for several deformable objects,
e.g. the human body, it is difficult to define a consistent set of landmarks. Even though, the SDMs
construction still suffers in terms of sparse landmark annotation. With sparse annotations, the shape
deformation of any deformable objects is only captured where the key points are labeled, thus nuanced
shape deformation is discarded. It is merely possible to accurately annotate dense shape deforestation
manually.
Fortunately, for the majority of objects, it is possible to extract the shape by object segmentation or even
by shape drawing. In this thesis, we propose that it is possible to construct SDMs by putting objects
shapes in dense correspondence. Such SDMs can be built with much less effort for a large battery of
objects. Additionally, by sampling the dense model, a part-based SDM can be learned with its parts
being in correspondence. We propose a 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 articulated objects. Such a system can serve as an element of cascaded
architectures that jointly localize landmarks and estimate dense correspondences. Also, we show that the
obtained dense correspondence can act as a source of prior knowledge that complements and extends the
pure landmark-level annotations, accelerating and improving the training of pose estimation networks.
The thesis contributes towards fully automatic dense correspondence estimation. Such correspondence
would benefit any system that requires nuance shape deformation. We are approaching a milestone that
dense correspondence and poses for deformable objects can be estimated “in-the-wild” with minimal
effort on labeling. The estimated correspondence is shown to be useful for various downstream tasks for
a collection of objects.
Version
Open Access
Date Issued
2019-02
Date Awarded
2019-10
Copyright Statement
Creative Commons Attribution NonCommercial Licence
Advisor
Zafeiriou, Stefanos
Publisher Department
Computing
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)