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3D shape editing is widely used in a range of applications such as movieproduction, computer games and computer aided design. It is also a popularresearch topic in computer graphics and computer vision. In past decades,researchers have developed a series of editing methods to make the editingprocess faster, more robust, and more reliable. Traditionally, the deformedshape is determined by the optimal transformation and weights for an energyterm. With increasing availability of 3D shapes on the Internet, data-drivenmethods were proposed to improve the editing results. More recently as the deepneural networks became popular, many deep learning based editing methods havebeen developed in this field, which is naturally data-driven. We mainly surveyrecent research works from the geometric viewpoint to those emerging neuraldeformation techniques and categorize them into organic shape editing methodsand man-made model editing methods. Both traditional methods and recent neuralnetwork based methods are reviewed.

A Revisit of Shape Editing Techniques: from the Geometric to the Neural Viewpoint