3D model deformation has been an active research topic in geometric processing. Due to its efficiency, linear blend skinning (LBS) and its follow-up methods are widely used in practical applications as an efficient method for deforming vector images, geometric models and animated characters. LBS needs to determine the control handles and specify their influence weights, which requires expertise and is time-consuming. Further studies have proposed a method for efficiently calculating bounded biharmonic weights of given control handles which reduces user effort and produces smooth deformation results. The algorithm defines a high-order shape-aware smoothness function which tends to produce smooth deformation results, but fails to generate locally rigid deformations.

To address this, we propose a novel data-driven approach to producing improved weights for handles that makes full use of available 3D model data by optimizing an energy consisting of data-driven, rigidity and sparsity terms, while maintaining its advantage of allowing handles of various forms. We further devise an efficient iterative optimization scheme. Through contrast experiments, it clearly shows that linear blend skinning based on our optimized weights better reflects the deformation characteristics of the model, leading to more accurate deformation results, outperforming existing methods. The method also retains real-time performance even with a large number of deformation examples. Our ablation experiments also show that each energy term is essential.

3D模型变形一直是几何处理中的活跃研究主题。由于其效率，线性混合蒙皮（LBS）及其后续方法在实际应用中被广泛用作使矢量图像，几何模型和动画角色变形的有效方法。LBS需要确定控制手柄并指定其影响权重，这需要专业知识并且很耗时。进一步的研究提出了一种有效计算给定控制手柄的有界双谐波权重的方法，该方法减少了用户的工作量并产生了平滑的变形结果。该算法定义了一个高阶形状感知平滑函数，该函数倾向于产生平滑的变形结果，但是无法生成局部刚性变形。

为了解决这个问题，我们提出了一种新颖的数据驱动方法来为手柄产生更好的权重，该方法通过优化由数据驱动的，刚性和稀疏性项组成的能量来充分利用可用的3D模型数据，同时保持了允许使用手柄的优势。各种形式。我们进一步设计了一种有效的迭代优化方案。通过对比实验，它清楚地表明，基于我们优化的权重的线性混合蒙皮可以更好地反映模型的变形特征，从而导致更准确的变形结果，优于现有方法。即使存在大量变形示例，该方法也保留了实时性能。我们的消融实验还表明，每个能量项都是必不可少的。