Semantic-Aware Implicit Template Learning via Part Deformation
  Consistency
Sihyeon Kim Minseok Joo Jaewon Lee Juyeon Ko Juhan Cha Hyunwoo J. Kim
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.