Kavli Affiliate: Matthew Fisher
| First 5 Authors: Difan Liu, Matthew Fisher, Aaron Hertzmann, Evangelos Kalogerakis,
| Summary:
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.
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