Establishing character shape correspondence is a critical and fundamental task in computer vision and graphics, with diverse applications including re-topology, attribute transfer, and shape interpolation. Current dominant functional map methods, while effective in controlled scenarios, struggle in real situations with more complex challenges such as non-isometric shape discrepancies. In response, we revisit registration-for-correspondence methods and tap their potential for more stable shape correspondence estimation. To overcome their common issues including unstable deformations and the necessity for careful pre-alignment or high-quality initial 3D correspondences, we introduce Stable-SCore: A Stable Registration-based Framework for 3D Shape Correspondence. We first re-purpose a foundation model for 2D character correspondence that ensures reliable and stable 2D mappings. Crucially, we propose a novel Semantic Flow Guided Registration approach that leverages 2D correspondence to guide mesh deformations. Our framework significantly surpasses existing methods in challenging scenarios, and brings possibilities for a wide array of real applications, as demonstrated in our results.

翻译：建立角色形状对应是计算机视觉与图形学中关键且基础的任务，在重拓扑、属性迁移和形状插值等应用中具有广泛用途。当前主流的函数映射方法虽然在受控场景下有效，但在实际面对非等距形状差异等更复杂挑战时往往表现不佳。为此，我们重新审视基于配准的对应方法，挖掘其在实现更稳定形状对应估计方面的潜力。为克服此类方法普遍存在的不稳定形变、需要精细预对齐或高质量初始三维对应等问题，我们提出了Stable-SCore：一种基于稳定配准的三维形状对应框架。我们首先改造了一个用于二维角色对应的基础模型，以确保可靠稳定的二维映射。关键的是，我们提出了一种新颖的语义流引导配准方法，利用二维对应关系来指导网格形变。实验结果表明，我们的框架在具有挑战性的场景中显著超越了现有方法，并为广泛的实际应用带来了可能性。