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Learning implicit templates as neural fields has recently shown impressiveperformance in unsupervised shape correspondence. Despite the success, weobserve current approaches, which solely rely on geometric information, oftenlearn suboptimal deformation across generic object shapes, which have highstructural variability. In this paper, we highlight the importance of partdeformation consistency and propose a semantic-aware implicit template learningframework to enable semantically plausible deformation. By leveraging semanticprior from a self-supervised feature extractor, we suggest local conditioningwith novel semantic-aware deformation code and deformation consistencyregularizations regarding part deformation, global deformation, and globalscaling. Our extensive experiments demonstrate the superiority of the proposedmethod over baselines in various tasks: keypoint transfer, part label transfer,and texture transfer. More interestingly, our framework shows a largerperformance gain under more challenging settings. We also provide qualitativeanalyses to validate the effectiveness of semantic-aware deformation. The codeis available at https://github.com/mlvlab/PDC.

Semantic-Aware Implicit Template Learning via Part Deformation Consistency