DeepShapeSketch : Generating hand drawing sketches from 3D objects

Abstract

Freehand sketches are an important medium for expressing and communicating ideas. However creating a meaningful and understandable sketch drawing is not always an easy task especially for unskillful users. Existing methods for rendering 3D shape into line drawings such as Suggestive Contours, only consider the geometry-dependent and view-dependent information thus leads to over-regular or over-perfect results which doesn’t look like a human freehand drawing. For this challenge we address the problem of producing freehand line drawing sketches from a 3D object under a given viewpoint automatically. The core solution here is a recurrent generative deep neural network, which learns a functional mapping from the suggestive contours of a 3D shape to a more abstract sketch representation. We drop the encoder of the generator, i.e., use only a decoder to achieve better stability of the sketch structure. Users can tune the level of freehand style of the generated sketches by changing a single parameter. Experiments show that our results are expressive enough to faithfully describe the input shape and at the same time be with the style of freehand drawings created by a real human. We also perform a comparative user study to verify the quality and style of generated sketch results over existing methods. We also retrain our network using several different mingled dataset to test the extendibility of our method for this particular application. As far as our knowledge this work is the first research effort to automate the generation of human-like freehand sketches directly from 3D shapes.