Sketch2Relief: Generating Bas-relief from Sketches with Deep Generative Networks

Shizhe Zhou, Zeyu Liu

Abstract: We present a novel sketch-based system for generating digital bas-relief sculptures. All existing computational methods for generating digital bas-reliefs first require the input of a three-dimensional (3D) scene, thus preventing artists from freely creating or exploring designs when 3D data are not available. Motivated by this limitation, we propose a generative adversarial network (GAN)-based sketch modeling system for generating digital bas-reliefs from freehand user sketches (see Figure 1, 5). The basic tool underpinning the interface is a conditional GAN (cGAN) that digitally learns a functional map from a contour image to a 3D model for any given viewpoint of the corresponding bas-relief model. When using our system for designing bas-reliefs, the user only needs to draw 2D sketch lines without having to designate any additional hints on the lines. The interface returns bas-relief results in interactive time (500 ms per bas-relief on average). We tested the quality and robustness of our approach with extensive and comprehensive experiments. By carefully analyzing the results, we verified that our system can faithfully reconstruct bas-reliefs from a test dataset and can generate completely new reliefs from raw amateur sketches.

External IDs:dblp:conf/ictai/ZhouL20