3Doodle: Compact Abstraction of Objects with 3D Strokes
Changwoon Choi Jaeah Lee Jaesik Park Young Min Kim
While free-hand sketching has long served as an efficient representation to
convey characteristics of an object, they are often subjective, deviating
significantly from realistic representations. Moreover, sketches are not
consistent for arbitrary viewpoints, making it hard to catch 3D shapes. We
propose 3Dooole, generating descriptive and view-consistent sketch images given
multi-view images of the target object. Our method is based on the idea that a
set of 3D strokes can efficiently represent 3D structural information and
render view-consistent 2D sketches. We express 2D sketches as a union of
view-independent and view-dependent components. 3D cubic B ezier curves
indicate view-independent 3D feature lines, while contours of superquadrics
express a smooth outline of the volume of varying viewpoints. Our pipeline
directly optimizes the parameters of 3D stroke primitives to minimize
perceptual losses in a fully differentiable manner. The resulting sparse set of
3D strokes can be rendered as abstract sketches containing essential 3D
characteristic shapes of various objects. We demonstrate that 3Doodle can
faithfully express concepts of the original images compared with recent sketch
generation approaches.