高保真可解释的逆Rig：优化四次Blendshape模型的精确稀疏解
High-fidelity Interpretable Inverse Rig: An Accurate and Sparse Solution Optimizing the Quartic Blendshape Model
摘要：
本文提出了一种通过求解真实感人脸动画中的rig逆问题来拟合任意精度blendshape rig模型的方法。该方法考虑具有不同水平的添加校正的融合变形模型，并使用坐标下降来解决正则化最小二乘问题，即，迭代地估计混合变形权重。除了使优化更容易解决，这种方法确保互斥控制器不会被同时激活，并提高每次迭代后的拟合优度。我们的实验表明，所提出的方法产生的解决方案与网格误差可比或低于国家的最先进的方法，同时显着降低权重向量的基数（超过20%），从而提供了一个高保真度重建的参考表达式，更容易在后期制作手动操作。该算法的Python脚本将在论文被接受后公开提供。
英文摘要：
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.
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。
