带二次修正项的实际Blendshape模型逆Rig的精确解
Accurate and Interpretable Solution of the Inverse Rig for Realistic Blendshape Models with Quadratic Corrective Terms
摘要：
我们提出了一种新的基于模型的算法，解决了面部动画重定向中的逆钻机问题，表现出更高的精度的拟合和稀疏，更可解释的权重向量相比，SOTA。该方法针对人脸动画的一个特定子域-用于电影和视频游戏制作的高度逼真的blendshape模型。在本文中，我们制定了一个优化问题，考虑到目标模型的所有要求。我们的目标超越了线性blendshape模型，并采用了二次校正项正确拟合网格的细节。我们表明，即使使用通用的求解器，如SQP，所提出的问题的解决方案产生高度准确的网格重建。使用SQP得到的结果是高度准确的网格空间，但没有表现出良好的质量，在重量稀疏性和光滑性，为此，我们进一步提出了一种新的算法依赖于MM技术。该算法特别适合于解决所提出的目标，产生高精度的网格拟合，同时尊重约束条件，并产生一组稀疏和平滑的权重，易于操作和解释的艺术家。我们的算法是基准与SOTA的方法，并显示出整体的优越性的结果，产生一个平滑的动画重建的相对改善高达45%的均方根网格误差，同时保持基数可比的基准方法。本文给出了一套全面的评估指标，涵盖了解决方案的不同方面，包括网格精度，权重的稀疏性和动画曲线的平滑度，以及人类专家评估的所生成动画的外观。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
