3,572 research outputs found
4D Facial Expression Diffusion Model
Facial expression generation is one of the most challenging and long-sought
aspects of character animation, with many interesting applications. The
challenging task, traditionally having relied heavily on digital craftspersons,
remains yet to be explored. In this paper, we introduce a generative framework
for generating 3D facial expression sequences (i.e. 4D faces) that can be
conditioned on different inputs to animate an arbitrary 3D face mesh. It is
composed of two tasks: (1) Learning the generative model that is trained over a
set of 3D landmark sequences, and (2) Generating 3D mesh sequences of an input
facial mesh driven by the generated landmark sequences. The generative model is
based on a Denoising Diffusion Probabilistic Model (DDPM), which has achieved
remarkable success in generative tasks of other domains. While it can be
trained unconditionally, its reverse process can still be conditioned by
various condition signals. This allows us to efficiently develop several
downstream tasks involving various conditional generation, by using expression
labels, text, partial sequences, or simply a facial geometry. To obtain the
full mesh deformation, we then develop a landmark-guided encoder-decoder to
apply the geometrical deformation embedded in landmarks on a given facial mesh.
Experiments show that our model has learned to generate realistic, quality
expressions solely from the dataset of relatively small size, improving over
the state-of-the-art methods. Videos and qualitative comparisons with other
methods can be found at https://github.com/ZOUKaifeng/4DFM. Code and models
will be made available upon acceptance
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PDE Face: A Novel 3D Face Model
YesWe introduce a novel approach to face models, which
exploits the use of Partial Differential Equations (PDE) to
generate the 3D face. This addresses some common
problems of existing face models. The PDE face benefits
from seamless merging of surface patches by using only a
relatively small number of parameters based on boundary
curves. The PDE face also provides users with a great
degree of freedom to individualise the 3D face by
adjusting a set of facial boundary curves. Furthermore, we
introduce a uv-mesh texture mapping method. By
associating the texels of the texture map with the vertices
of the uv mesh in the PDE face, the new texture mapping
method eliminates the 3D-to-2D association routine in
texture mapping. Any specific PDE face can be textured
without the need for the facial expression in the texture
map to match exactly that of the 3D face model
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Highly automated method for facial expression synthesis
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The synthesis of realistic facial expressions has been an unexplored area for computer graphics scientists. Over the last three decades, several different construction methods have been formulated in order to obtain natural graphic results. Despite these advancements, though, current techniques still require costly resources, heavy user intervention and specific training and outcomes are still not completely realistic. This thesis, therefore, aims to achieve an automated synthesis that will produce realistic facial expressions at a low cost.
This thesis, proposes a highly automated approach for achieving a realistic facial
expression synthesis, which allows for enhanced performance in speed (3 minutes
processing time maximum) and quality with a minimum of user intervention. It will also demonstrate a highly technical and automated method of facial feature detection, by allowing users to obtain their desired facial expression synthesis with minimal
physical input. Moreover, it will describe a novel approach to the normalization of the
illumination settings values between source and target images, thereby allowing the
algorithm to work accurately, even in different lighting conditions.
Finally, we will present the results obtained from the proposed techniques, together with our conclusions, at the end of the paper
Final Report to NSF of the Standards for Facial Animation Workshop
The human face is an important and complex communication channel. It is a very familiar and sensitive object of human perception. The facial animation field has increased greatly in the past few years as fast computer graphics workstations have made the modeling and real-time animation of hundreds of thousands of polygons affordable and almost commonplace. Many applications have been developed such as teleconferencing, surgery, information assistance systems, games, and entertainment. To solve these different problems, different approaches for both animation control and modeling have been developed
Reconstruction of 3D human facial images using partial differential equations.
One of the challenging problems in geometric
modeling and computer graphics is the construction of
realistic human facial geometry. Such geometry are
essential for a wide range of applications, such as 3D face
recognition, virtual reality applications, facial expression
simulation and computer based plastic surgery application.
This paper addresses a method for the construction of 3D
geometry of human faces based on the use of Elliptic Partial
Differential Equations (PDE). Here the geometry
corresponding to a human face is treated as a set of surface
patches, whereby each surface patch is represented using
four boundary curves in the 3-space that formulate the
appropriate boundary conditions for the chosen PDE. These
boundary curves are extracted automatically using 3D data
of human faces obtained using a 3D scanner. The solution of
the PDE generates a continuous single surface patch
describing the geometry of the original scanned data. In this
study, through a number of experimental verifications we
have shown the efficiency of the PDE based method for 3D
facial surface reconstruction using scan data. In addition to
this, we also show that our approach provides an efficient
way of facial representation using a small set of parameters
that could be utilized for efficient facial data storage and
verification purposes
Geometric Expression Invariant 3D Face Recognition using Statistical Discriminant Models
Currently there is no complete face recognition system that is invariant to all facial expressions.
Although humans find it easy to identify and recognise faces regardless of changes in illumination,
pose and expression, producing a computer system with a similar capability has proved to
be particularly di cult. Three dimensional face models are geometric in nature and therefore
have the advantage of being invariant to head pose and lighting. However they are still susceptible
to facial expressions. This can be seen in the decrease in the recognition results using
principal component analysis when expressions are added to a data set.
In order to achieve expression-invariant face recognition systems, we have employed a tensor
algebra framework to represent 3D face data with facial expressions in a parsimonious
space. Face variation factors are organised in particular subject and facial expression modes.
We manipulate this using single value decomposition on sub-tensors representing one variation
mode. This framework possesses the ability to deal with the shortcomings of PCA in less constrained
environments and still preserves the integrity of the 3D data. The results show improved
recognition rates for faces and facial expressions, even recognising high intensity expressions
that are not in the training datasets.
We have determined, experimentally, a set of anatomical landmarks that best describe facial
expression e ectively. We found that the best placement of landmarks to distinguish di erent
facial expressions are in areas around the prominent features, such as the cheeks and eyebrows.
Recognition results using landmark-based face recognition could be improved with better placement.
We looked into the possibility of achieving expression-invariant face recognition by reconstructing
and manipulating realistic facial expressions. We proposed a tensor-based statistical
discriminant analysis method to reconstruct facial expressions and in particular to neutralise
facial expressions. The results of the synthesised facial expressions are visually more realistic
than facial expressions generated using conventional active shape modelling (ASM). We
then used reconstructed neutral faces in the sub-tensor framework for recognition purposes.
The recognition results showed slight improvement. Besides biometric recognition, this novel
tensor-based synthesis approach could be used in computer games and real-time animation
applications
THREE DIMENSIONAL MODELING AND ANIMATION OF FACIAL EXPRESSIONS
Facial expression and animation are important aspects of the 3D environment featuring human characters. These animations are frequently used in many kinds of applications and there have been many efforts to increase the realism. Three aspects are still stimulating active research: the detailed subtle facial expressions, the process of rigging a face, and the transfer of an expression from one person to another. This dissertation focuses on the above three aspects.
A system for freely designing and creating detailed, dynamic, and animated facial expressions is developed. The presented pattern functions produce detailed and animated facial expressions. The system produces realistic results with fast performance, and allows users to directly manipulate it and see immediate results.
Two unique methods for generating real-time, vivid, and animated tears have been developed and implemented. One method is for generating a teardrop that continually changes its shape as the tear drips down the face. The other is for generating a shedding tear, which is a kind of tear that seamlessly connects with the skin as it flows along the surface of the face, but remains an individual object. The methods both broaden CG and increase the realism of facial expressions.
A new method to automatically set the bones on facial/head models to speed up the rigging process of a human face is also developed. To accomplish this, vertices that describe the face/head as well as relationships between each part of the face/head are grouped. The average distance between pairs of vertices is used to place the head bones. To set the bones in the face with multi-density, the mean value of the vertices in a group is measured. The time saved with this method is significant.
A novel method to produce realistic expressions and animations by transferring an existing expression to a new facial model is developed. The approach is to transform the source model into the target model, which then has the same topology as the source model. The displacement vectors are calculated. Each vertex in the source model is mapped to the target model. The spatial relationships of each mapped vertex are constrained
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