  Seam carving is an image editing method that enable content-aware resizing,
including operations like removing objects. However, the seam-finding strategy
based on dynamic programming or graph-cut limits its applications to broader
visual data formats and degrees of freedom for editing. Our observation is that
describing the editing and retargeting of images more generally by a
displacement field yields a generalisation of content-aware deformations. We
propose to learn a deformation with a neural network that keeps the output
plausible while trying to deform it only in places with low information
content. This technique applies to different kinds of visual data, including
images, 3D scenes given as neural radiance fields, or even polygon meshes.
Experiments conducted on different visual data show that our method achieves
better content-aware retargeting compared to previous methods.
