Neural Strokes: Stylized Line Drawing of 3D Shapes

ICCV 2021

Abstract

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.

Paper

NeuralStrokes.pdf, 11.5MB

Video

Source Code & Data

Github code: https://github.com/DifanLiu/NeuralStrokes

Acknowledgements

This research is partially funded by NSF (CHS-1617333) and Adobe. We thank Jonathan Eisenmann and Shayan Hoshyari for helpful discussions. We also thank the artists who contributed to our dataset.