144 research outputs found
Modeling Caricature Expressions by 3D Blendshape and Dynamic Texture
The problem of deforming an artist-drawn caricature according to a given
normal face expression is of interest in applications such as social media,
animation and entertainment. This paper presents a solution to the problem,
with an emphasis on enhancing the ability to create desired expressions and
meanwhile preserve the identity exaggeration style of the caricature, which
imposes challenges due to the complicated nature of caricatures. The key of our
solution is a novel method to model caricature expression, which extends
traditional 3DMM representation to caricature domain. The method consists of
shape modelling and texture generation for caricatures. Geometric optimization
is developed to create identity-preserving blendshapes for reconstructing
accurate and stable geometric shape, and a conditional generative adversarial
network (cGAN) is designed for generating dynamic textures under target
expressions. The combination of both shape and texture components makes the
non-trivial expressions of a caricature be effectively defined by the extension
of the popular 3DMM representation and a caricature can thus be flexibly
deformed into arbitrary expressions with good results visually in both shape
and color spaces. The experiments demonstrate the effectiveness of the proposed
method.Comment: Accepted by the 28th ACM International Conference on Multimedia (ACM
MM 2020
High-fidelity Interpretable Inverse Rig: An Accurate and Sparse Solution Optimizing the Quartic Blendshape Model
We propose a method to fit arbitrarily accurate blendshape rig models by
solving the inverse rig problem in realistic human face animation. The method
considers blendshape models with different levels of added corrections and
solves the regularized least-squares problem using coordinate descent, i.e.,
iteratively estimating blendshape weights. Besides making the optimization
easier to solve, this approach ensures that mutually exclusive controllers will
not be activated simultaneously and improves the goodness of fit after each
iteration. We show experimentally that the proposed method yields solutions
with mesh error comparable to or lower than the state-of-the-art approaches
while significantly reducing the cardinality of the weight vector (over 20
percent), hence giving a high-fidelity reconstruction of the reference
expression that is easier to manipulate in the post-production manually. Python
scripts for the algorithm will be publicly available upon acceptance of the
paper
Genetic algorithms reveal identity independent representation of emotional expressions
People readily and automatically process facial emotion and identity, and it has been reported that these cues are processed both dependently and independently. However, this question of identity independent encoding of emotions has only been examined using posed, often exaggerated expressions of emotion, that do not account for the substantial individual differences in emotion recognition. In this study, we ask whether people's unique beliefs of how emotions should be reflected in facial expressions depend on the identity of the face. To do this, we employed a genetic algorithm where participants created facial expressions to represent different emotions. Participants generated facial expressions of anger, fear, happiness, and sadness, on two different identities. Facial features were controlled by manipulating a set of weights, allowing us to probe the exact positions of faces in high-dimensional expression space. We found that participants created facial expressions belonging to each identity in a similar space that was unique to the participant, for angry, fearful, and happy expressions, but not sad. However, using a machine learning algorithm that examined the positions of faces in expression space, we also found systematic differences between the two identities' expressions across participants. This suggests that participants' beliefs of how an emotion should be reflected in a facial expression are unique to them and identity independent, although there are also some systematic differences in the facial expressions between two identities that are common across all individuals. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
HeadOn: Real-time Reenactment of Human Portrait Videos
We propose HeadOn, the first real-time source-to-target reenactment approach
for complete human portrait videos that enables transfer of torso and head
motion, face expression, and eye gaze. Given a short RGB-D video of the target
actor, we automatically construct a personalized geometry proxy that embeds a
parametric head, eye, and kinematic torso model. A novel real-time reenactment
algorithm employs this proxy to photo-realistically map the captured motion
from the source actor to the target actor. On top of the coarse geometric
proxy, we propose a video-based rendering technique that composites the
modified target portrait video via view- and pose-dependent texturing, and
creates photo-realistic imagery of the target actor under novel torso and head
poses, facial expressions, and gaze directions. To this end, we propose a
robust tracking of the face and torso of the source actor. We extensively
evaluate our approach and show significant improvements in enabling much
greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at
Siggraph'1
Accurate and Interpretable Solution of the Inverse Rig for Realistic Blendshape Models with Quadratic Corrective Terms
We propose a new model-based algorithm solving the inverse rig problem in
facial animation retargeting, exhibiting higher accuracy of the fit and
sparser, more interpretable weight vector compared to SOTA. The proposed method
targets a specific subdomain of human face animation - highly-realistic
blendshape models used in the production of movies and video games. In this
paper, we formulate an optimization problem that takes into account all the
requirements of targeted models. Our objective goes beyond a linear blendshape
model and employs the quadratic corrective terms necessary for correctly
fitting fine details of the mesh. We show that the solution to the proposed
problem yields highly accurate mesh reconstruction even when general-purpose
solvers, like SQP, are used. The results obtained using SQP are highly accurate
in the mesh space but do not exhibit favorable qualities in terms of weight
sparsity and smoothness, and for this reason, we further propose a novel
algorithm relying on a MM technique. The algorithm is specifically suited for
solving the proposed objective, yielding a high-accuracy mesh fit while
respecting the constraints and producing a sparse and smooth set of weights
easy to manipulate and interpret by artists. Our algorithm is benchmarked with
SOTA approaches, and shows an overall superiority of the results, yielding a
smooth animation reconstruction with a relative improvement up to 45 percent in
root mean squared mesh error while keeping the cardinality comparable with
benchmark methods. This paper gives a comprehensive set of evaluation metrics
that cover different aspects of the solution, including mesh accuracy, sparsity
of the weights, and smoothness of the animation curves, as well as the
appearance of the produced animation, which human experts evaluated
Genetic algorithms reveal identity independent representation of emotional expressions.
People readily and automatically process facial emotion and identity, and it has been reported that these cues are processed both dependently and independently. However, this question of identity independent encoding of emotions has only been examined using posed, often exaggerated expressions of emotion, that do not account for the substantial individual differences in emotion recognition. In this study, we ask whether people's unique beliefs of how emotions should be reflected in facial expressions depend on the identity of the face. To do this, we employed a genetic algorithm where participants created facial expressions to represent different emotions. Participants generated facial expressions of anger, fear, happiness, and sadness, on two different identities. Facial features were controlled by manipulating a set of weights, allowing us to probe the exact positions of faces in high-dimensional expression space. We found that participants created facial expressions belonging to each identity in a similar space that was unique to the participant, for angry, fearful, and happy expressions, but not sad. However, using a machine learning algorithm that examined the positions of faces in expression space, we also found systematic differences between the two identities' expressions across participants. This suggests that participants' beliefs of how an emotion should be reflected in a facial expression are unique to them and identity independent, although there are also some systematic differences in the facial expressions between two identities that are common across all individuals. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
Distributed Solution of the Inverse Rig Problem in Blendshape Facial Animation
The problem of rig inversion is central in facial animation as it allows for
a realistic and appealing performance of avatars. With the increasing
complexity of modern blendshape models, execution times increase beyond
practically feasible solutions. A possible approach towards a faster solution
is clustering, which exploits the spacial nature of the face, leading to a
distributed method. In this paper, we go a step further, involving cluster
coupling to get more confident estimates of the overlapping components. Our
algorithm applies the Alternating Direction Method of Multipliers, sharing the
overlapping weights between the subproblems. The results obtained with this
technique show a clear advantage over the naive clustered approach, as measured
in different metrics of success and visual inspection. The method applies to an
arbitrary clustering of the face. We also introduce a novel method for choosing
the number of clusters in a data-free manner. The method tends to find a
clustering such that the resulting clustering graph is sparse but without
losing essential information. Finally, we give a new variant of a data-free
clustering algorithm that produces good scores with respect to the mentioned
strategy for choosing the optimal clustering
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