1,186 research outputs found
Redundancy reduction in 3D facial motion capture data for animation
Research on the perception of dynamic faces often requires real-time animations with low latency. With an adaptation of principal feature analysis [Cohen et al. 2002], we can reduce the number of facial motion capture markers by 50, while retaining the overall animation quality
Human Motion Capture Data Tailored Transform Coding
Human motion capture (mocap) is a widely used technique for digitalizing
human movements. With growing usage, compressing mocap data has received
increasing attention, since compact data size enables efficient storage and
transmission. Our analysis shows that mocap data have some unique
characteristics that distinguish themselves from images and videos. Therefore,
directly borrowing image or video compression techniques, such as discrete
cosine transform, does not work well. In this paper, we propose a novel
mocap-tailored transform coding algorithm that takes advantage of these
features. Our algorithm segments the input mocap sequences into clips, which
are represented in 2D matrices. Then it computes a set of data-dependent
orthogonal bases to transform the matrices to frequency domain, in which the
transform coefficients have significantly less dependency. Finally, the
compression is obtained by entropy coding of the quantized coefficients and the
bases. Our method has low computational cost and can be easily extended to
compress mocap databases. It also requires neither training nor complicated
parameter setting. Experimental results demonstrate that the proposed scheme
significantly outperforms state-of-the-art algorithms in terms of compression
performance and speed
Low-latency compression of mocap data using learned spatial decorrelation transform
Due to the growing needs of human motion capture (mocap) in movie, video
games, sports, etc., it is highly desired to compress mocap data for efficient
storage and transmission. This paper presents two efficient frameworks for
compressing human mocap data with low latency. The first framework processes
the data in a frame-by-frame manner so that it is ideal for mocap data
streaming and time critical applications. The second one is clip-based and
provides a flexible tradeoff between latency and compression performance. Since
mocap data exhibits some unique spatial characteristics, we propose a very
effective transform, namely learned orthogonal transform (LOT), for reducing
the spatial redundancy. The LOT problem is formulated as minimizing square
error regularized by orthogonality and sparsity and solved via alternating
iteration. We also adopt a predictive coding and temporal DCT for temporal
decorrelation in the frame- and clip-based frameworks, respectively.
Experimental results show that the proposed frameworks can produce higher
compression performance at lower computational cost and latency than the
state-of-the-art methods.Comment: 15 pages, 9 figure
Surface Reconstruction and Evolution from Multiple Views
Applications like 3D Telepresence necessitate faithful 3D surface reconstruction
of the object and 3D data compression in both spatial and
temporal domains. This makes us feel immersed in virtual environments
there by making 3D Telepresence a powerful tool in many applications.
Hence 3D surface reconstruction and 3D compression are two challenging
problems which are addressed in this thesis
Statistical modelling for facial expression dynamics
PhDOne of the most powerful and fastest means of relaying emotions between humans are facial expressions.
The ability to capture, understand and mimic those emotions and their underlying dynamics
in the synthetic counterpart is a challenging task because of the complexity of human emotions, different
ways of conveying them, non-linearities caused by facial feature and head motion, and the
ever critical eye of the viewer. This thesis sets out to address some of the limitations of existing
techniques by investigating three components of expression modelling and parameterisation framework:
(1) Feature and expression manifold representation, (2) Pose estimation, and (3) Expression
dynamics modelling and their parameterisation for the purpose of driving a synthetic head avatar.
First, we introduce a hierarchical representation based on the Point Distribution Model (PDM).
Holistic representations imply that non-linearities caused by the motion of facial features, and intrafeature
correlations are implicitly embedded and hence have to be accounted for in the resulting
expression space. Also such representations require large training datasets to account for all possible
variations. To address those shortcomings, and to provide a basis for learning more subtle, localised
variations, our representation consists of tree-like structure where a holistic root component is decomposed
into leaves containing the jaw outline, each of the eye and eyebrows and the mouth. Each
of the hierarchical components is modelled according to its intrinsic functionality, rather than the
final, holistic expression label.
Secondly, we introduce a statistical approach for capturing an underlying low-dimension expression
manifold by utilising components of the previously defined hierarchical representation. As
Principal Component Analysis (PCA) based approaches cannot reliably capture variations caused by
large facial feature changes because of its linear nature, the underlying dynamics manifold for each
of the hierarchical components is modelled using a Hierarchical Latent Variable Model (HLVM) approach.
Whilst retaining PCA properties, such a model introduces a probability density model which
can deal with missing or incomplete data and allows discovery of internal within cluster structures.
All of the model parameters and underlying density model are automatically estimated during the
training stage. We investigate the usefulness of such a model to larger and unseen datasets.
Thirdly, we extend the concept of HLVM model to pose estimation to address the non-linear
shape deformations and definition of the plausible pose space caused by large head motion. Since
our head rarely stays still, and its movements are intrinsically connected with the way we perceive
and understand the expressions, pose information is an integral part of their dynamics. The proposed
3
approach integrates into our existing hierarchical representation model. It is learned using sparse and
discreetly sampled training dataset, and generalises to a larger and continuous view-sphere.
Finally, we introduce a framework that models and extracts expression dynamics. In existing
frameworks, explicit definition of expression intensity and pose information, is often overlooked,
although usually implicitly embedded in the underlying representation. We investigate modelling
of the expression dynamics based on use of static information only, and focus on its sufficiency
for the task at hand. We compare a rule-based method that utilises the existing latent structure and
provides a fusion of different components with holistic and Bayesian Network (BN) approaches. An
Active Appearance Model (AAM) based tracker is used to extract relevant information from input
sequences. Such information is subsequently used to define the parametric structure of the underlying
expression dynamics. We demonstrate that such information can be utilised to animate a synthetic
head avatar.
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A Review on Facial Expression Recognition Techniques
Facial expression is in the topic of active research over the past few decades. Recognition and extracting various emotions and validating those emotions from the facial expression become very important in human computer interaction. Interpreting such human expression remains and much of the research is required about the way they relate to human affect. Apart from H-I interfaces other applications include awareness system, medical diagnosis, surveillance, law enforcement, automated tutoring system and many more. In the recent year different technique have been put forward for developing automated facial expression recognition system. This paper present quick survey on some of the facial expression recognition techniques. A comparative study is carried out using various feature extraction techniques. We define taxonomy of the field and cover all the steps from face detection to facial expression classification
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