This paper introduces a model for producing stylized line drawings from 3D shapes. The model takes a 3D shape and a viewpoint as input, and outputs a drawing with textured strokes, with variations in stroke thickness, deformation, and color learned from an artist's style. The model is fully differentiable. We train its parameters from a single training drawing of another 3D shape. We show that, in contrast to previous image-based methods, the use of a geometric representation of 3D shape and 2D strokes allows the model to transfer important aspects of shape and texture style while preserving contours. Our method outputs the resulting drawing in a vector representation, enabling richer downstream analysis or editing in interactive applications.
@article{arxiv.2110.03900,
title = {Neural Strokes: Stylized Line Drawing of 3D Shapes},
author = {Difan Liu and Matthew Fisher and Aaron Hertzmann and Evangelos Kalogerakis},
journal= {arXiv preprint arXiv:2110.03900},
year = {2021}
}