PaNDaS: Learnable Shape Interpolation Modeling with Localized Control

Abstract

We present PaNDaS, a novel deep learning framework for Partial Non-Rigid Deformations and interpolations of Surfaces (PaNDaS). PaNDaS learns a per-face feature field on the source mesh and fuses it with a global encoding of the target. A deformation generator predicts a Jacobian field and recovers a smooth displacement, enabling precise regional control, pose mixing, and transferable local edits. Unlike previous approaches, our method can restrict the deformations to specific parts of the shape in a versatile way. Across various human body part datasets, PaNDaS achieves state-of-the-art interpolation accuracy and stronger locality than methods based on global shape codes or handles, while remaining robust to remeshing. We demonstrate several localized shape manipulation tasks and show that our method can generate new shapes by combining different input deformations.