Accurate, Interpretable, and Fast Animation: AnIterative, Sparse, and
Nonconvex Approach
- URL: http://arxiv.org/abs/2109.08356v1
- Date: Fri, 17 Sep 2021 05:42:07 GMT
- Title: Accurate, Interpretable, and Fast Animation: AnIterative, Sparse, and
Nonconvex Approach
- Authors: Stevo Rackovic, Claudia Soares, Dusan Jakovetic and Zoranka Desnica
- Abstract summary: A face rig must be accurate and, at the same time, compute fast to solve the problem.
One of the parameters at each common animation model is a sparsity regularization.
In order to reduce the complexity, a paradigm Majorization Mini (MM) is applied.
- Score: 0.9176056742068814
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Digital human animation relies on high-quality 3D models of the human face:
rigs. A face rig must be accurate and, at the same time, fast to compute. One
of the most common rigging models is the blendshape model. We propose a novel
algorithm for solving the nonconvex inverse rig problem in facial animation.
Our approach is model-based, but in contrast with previous model-based
approaches, we use a quadratic instead of the linear approximation to the
higher order rig model. This increases the accuracy of the solution by 8
percent on average and, confirmed by the empirical results, increases the
sparsity of the resulting parameter vector -- an important feature for
interpretability by animation artists. The proposed solution is based on a
Levenberg-Marquardt (LM) algorithm, applied to a nonconvex constrained problem
with sparsity regularization. In order to reduce the complexity of the
iterates, a paradigm of Majorization Minimization (MM) is further invoked,
which leads to an easy to solve problem that is separable in the parameters at
each algorithm iteration. The algorithm is evaluated on a number of animation
datasets, proprietary and open-source, and the results indicate the superiority
of our method compared to the standard approach based on the linear rig
approximation. Although our algorithm targets the specific problem, it might
have additional signal processing applications.
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