RADmesh: Remesh-Aware Mesh Deformation

We propose a remeshing-enhanced method for generativelydeforming shapes with visual losses. It is intuitive that sufficiently dras-tic deformations of a mesh without changing its triangulation can easilycompromise element quality, even if such large geometry changes maybe semantically desired. Shape deformation methods could thus bene-fit from changing the triangulation; however, this is not done by mostgenerative, text-based, visually-supervised mesh deformation methods.Remeshing is a discrete operation, proven to be especially challengingto couple with the notoriously noisy supervision signal provided by vi-sual losses. We propose a vertex-based deformation optimization quantitycapable of large deformations and robustness to such noise; we period-ically remesh using an isotropic remesher that interpolates and carriesforward the deformation optimization state. This enables continuous,geometry-informed progress in coarse-to-fine addition of resolution. Theresulting shapes’ triangulations fit their optimized geometry and haveneat isotropic elements. Further, our method is localizable, able to grownew features on a base shape with expressive detail, leaving the rest un-changed. We showcase the effectiveness of our method on a variety ofshapes and prompts, both local and global deformations, and demon-strate its superior visual quality and triangle efficiency. Our project pageis at https://threedle.github.io/radmesh.