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.

翻译：图像接缝雕刻是一种支持内容感知缩放（包括移除物体等操作）的图像编辑方法。然而，基于动态规划或图割的接缝查找策略限制了其在更广泛视觉数据格式及编辑自由度上的应用。我们的观察是：通过位移场更一般地描述图像的编辑与重定向，能够实现内容感知变形的泛化。我们提出利用神经网络学习一种变形，在保持输出合理性的同时，仅对信息含量较低的区域进行变形。该技术适用于不同类型的视觉数据，包括图像、以神经辐射场形式给定的三维场景，甚至多边形网格。在不同视觉数据上进行的实验表明，与以往方法相比，我们的方法实现了更优的内容感知重定向。