Automatically mimicking unique hand-drawn pencil lines

Abstract

In applications such as architecture, early design sketches containing accurate line drawings often mislead the target audience [Schumann et al., 1996]. Approximate human-drawn sketches are typically accepted as a better way of demonstrating fundamental design concepts. To this end we have designed an algorithm that creates lines that perceptually resemble human-drawn lines. Our algorithm works directly with input point data and a physicallybased mathematical model of human arm movement. Our algorithm generates unique lines of arbitrary length given the end points of a line, without relying on a database of humandrawn lines. We found that an observational analysis obtained through various user studies of human lines made a bigger impact on the algorithm than a statistical analysis. Additional studies have shown that the algorithm produces lines that are perceptually indistinguishable from that of a hand-drawn straight pencil line. A further expansion to the system resulted in mimicked dashed lines.

Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty

“…These line models can, in turn, be applied to line renderers (e. g., [46,53]) to generate images that resemble-to varying degrees-traditional hand-drawn ones. More recently, NPR line models have been extended to be more examplebased, e. g., by taking the human arm movement into account in their generation [3]-a goal that we share for generating our sketchy lines.…”

“…AlMeraj et al [3] empirically defined the time sampling parameter δt according to the length of the desired line: δt = 0.5s for lines shorter than 200 pixels, δt = 0.3s for lines of 200-400 pixels, and δt = 0.2s for lines longer than 400 pixels. They also fixed t f = 2s and D within [−5, 5] pixels range based on empirical evidence.…”

Sketchy Rendering for Information Visualization

“…6). This was inspired by Gradwohl's [13] approach but we do not model line drawing with the sophistication of some NPR techniques (e. g., [1,37]). Our approach, instead, focuses on fast rendering, enabling interaction in a sketchy style.…”

Dynamic Graffiti Stylisation with Stochastic Optimal Control

“…House et al [26] generate sketchy renderings of a 3D model by using a Proportional Integral Derivative (PID) controller. AlMeraj et al [1] mimic the undulation of hand drawn pencil lines by using the minimum jerk model. Berio and Leymarie [6] use the sigma lognormal model [38] to interactively de ne the motion paths and variations of gra ti tag trajectories.…”

“…We describe a system that enables a user or an algorithm to rapidly de ne such traces through the speci cation of a control polygon made of a coarse sequence of targets. e user can then generate and interactively manipulate a family of motion trajectories, which follow the target layout (a) (1) (2) (1) is an original tag made with a marker by a gra ti artist; (2) a user rapidly sketches a control polygon by placing points (targets) near curvature extrema in overlay working from an image of the tag; (3) the user adjusts the Gaussians interactively to follow the trace of the original tag; (4) the reproduced tag rendered with a textured brush. (b) Variations on the tag by modifying the parameter d. (c) Variations on the tag generated from the speci ed targets using semi-tied covariances (illustrated as orientable yellow ellipses).…”