In this paper, we present an advanced approach to solving the inverse rig problem in blendshape animation, using high-quality corrective blendshapes. Our algorithm introduces novel enhancements in three key areas: ensuring high data fidelity in reconstructed meshes, achieving greater sparsity in weight distributions, and facilitating smoother frame-to-frame transitions. While the incorporation of corrective terms is a known practice, our method differentiates itself by employing a unique combination of $l_1$ norm regularization for sparsity and a temporal smoothness constraint through roughness penalty, focusing on the sum of second differences in consecutive frame weights. A significant innovation in our approach is the temporal decoupling of blendshapes, which permits simultaneous optimization across entire animation sequences. This feature sets our work apart from existing methods and contributes to a more efficient and effective solution. Our algorithm exhibits a marked improvement in maintaining data fidelity and ensuring smooth frame transitions when compared to prior approaches that either lack smoothness regularization or rely solely on linear blendshape models. In addition to superior mesh resemblance and smoothness, our method offers practical benefits, including reduced computational complexity and execution time, achieved through a novel parallelization strategy using clustering methods. Our results not only advance the state of the art in terms of fidelity, sparsity, and smoothness in inverse rigging but also introduce significant efficiency improvements. The source code will be made available upon acceptance of the paper.

翻译：本文提出了一种利用高质量修正融合变形来求解融合变形动画中逆蒙皮问题的先进方法。我们的算法在三个关键方面引入了新颖的增强：确保重建网格的高数据保真度、实现权重分布的更高稀疏性，以及促进更平滑的帧间过渡。虽然修正项的引入是一种已知做法，但我们的方法通过采用独特的$l_1$范数正则化以实现稀疏性，并结合粗糙度惩罚（聚焦于连续帧权重的二阶差分之和）实现时间平滑约束，从而脱颖而出。我们方法的一个重大创新是融合变形的时间解耦，这使得整个动画序列能够同时进行优化。这一特性使我们的工作有别于现有方法，并促成了更高效、更有效的解决方案。与那些缺乏平滑正则化或仅依赖线性融合变形模型的先前方法相比，我们的算法在保持数据保真度和确保平滑帧过渡方面展现出显著的改进。除了更优的网格相似度和平滑度之外，我们的方法还提供了实际好处，包括通过一种新颖的聚类方法并行化策略，降低了计算复杂度和执行时间。我们的结果不仅在逆蒙皮中的保真度、稀疏性和平滑度方面推进了现有技术水平，而且引入了显著的效率提升。源代码将在论文被接收后提供。