Learning implicit templates as neural fields has recently shown impressive performance in unsupervised shape correspondence. Despite the success, we observe current approaches, which solely rely on geometric information, often learn suboptimal deformation across generic object shapes, which have high structural variability. In this paper, we highlight the importance of part deformation consistency and propose a semantic-aware implicit template learning framework to enable semantically plausible deformation. By leveraging semantic prior from a self-supervised feature extractor, we suggest local conditioning with novel semantic-aware deformation code and deformation consistency regularizations regarding part deformation, global deformation, and global scaling. Our extensive experiments demonstrate the superiority of the proposed method over baselines in various tasks: keypoint transfer, part label transfer, and texture transfer. More interestingly, our framework shows a larger performance gain under more challenging settings. We also provide qualitative analyses to validate the effectiveness of semantic-aware deformation. The code is available at https://github.com/mlvlab/PDC.

本文介绍了一种语义感知隐式模板学习框架，通过利用自监督特征提取器的语义先验，提出了局部条件和全局条件的语义感知变形代码以及变形一致性正则化，从而实现了语义上合理的变形。通过大量实验证明了该方法在关键点转换、部位标签转换和纹理转换等各种任务中的优越性能，尤其在更具挑战性的任务中取得了更大的性能提升。

基于语义感知的隐式模板学习及部件变形的一致性