4 research outputs found
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
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
A Majorization-Minimization Based Method for Nonconvex Inverse Rig Problems in Facial Animation: Algorithm Derivation
Automated methods for facial animation are a necessary tool in the modern
industry since the standard blendshape head models consist of hundreds of
controllers and a manual approach is painfully slow. Different solutions have
been proposed that produce output in real-time or generalize well for different
face topologies. However, all these prior works consider a linear approximation
of the blendshape function and hence do not provide a high-enough level of
details for modern realistic human face reconstruction. We build a method for
solving the inverse rig in blendshape animation using quadratic corrective
terms, which increase accuracy. At the same time, due to the proposed
construction of the objective function, it yields a sparser estimated weight
vector compared to the state-of-the-art methods. The former feature means lower
demand for subsequent manual corrections of the solution, while the latter
indicates that the manual modifications are also easier to include. Our
algorithm is iterative and employs a Majorization Minimization paradigm to cope
with the increased complexity produced by adding the corrective terms. The
surrogate function is easy to solve and allows for further parallelization on
the component level within each iteration. This paper is complementary to an
accompanying paper, Rackovi\'c et al. (2023), where we provide detailed
experimental results and discussion, including highly-realistic animation data,
and show a clear superiority of the results compared to the state-of-the-art
methods