Perception-aware modeling and fabrication of digital drawing tools

Abstract

Digital drawing is becoming a favorite technique for many artists. It allows for quick swaps between different materials, reverting changes, and applying selective modifications to finished artwork. These features enable artists to be more efficient and creative. A significant disadvantage of digital drawing is poor haptic feedback. Artists are usually limited to one surface and a few different stylus nibs, and while they try to find a combination that suits their needs, this is typically challenging. In this work, we address this problem and propose a method for designing, evaluating, and optimizing different stylus designs. We begin with collecting a representative set of traditional drawing tools. We measure their physical properties and conduct a user experiment to build a perceptual space that encodes perceptually-relevant attributes of drawing materials. The space is optimized to both explain our experimental data and correlate it with measurable physical properties. To embed new drawing tool designs into the space without conducting additional experiments and measurements, we propose a new, data-driven simulation technique for characterizing stylus-surface interaction. We finally leverage the perceptual space, our simulation, and recent advancements in multi-material 3D printing to demonstrate the application of our system in the design of new digital drawing tools that mimic traditional drawing materials.

Tactile perception of randomly rough surfaces

“…For all experiments, the perceptual spaces of the ordinal MDS analysis were derived using a formulation provided in Ref. 21 . The method adapts a Bayesian approach 46 for converting pairwise comparisons into scalar data and finds an n-dimensional embedding of the samples by maximizing the probability of observing the experimental data.…”

“…To verify the reliability of the recovered perceptual spaces and its robustness to stochastic effects we used bootstrapping 21 . For each experiment, we generated new observations by randomly sampling the experimental data with repetition.…”

“…Participants were asked if "the left or the right sample is/feels more similar to the middle reference. " The list of all decisions was analyzed by means of ordinal multidimensional scaling analysis (MDS analysis), also referred to as non-metric MDS 21 . Readers not familiar with non-metric MDS analysis can find an instructive example in the method section.…”

“…The choice of three dimensions is justified by a cross-validation method which was introduced for a similar experimental design in Ref. 21 . Details are provided in the Methods section.…”

Fabrication-in-the-loop co-optimization of surfaces and styli for drawing haptics

“…First, to address the challenging co-optimization, we refrain from directly matching the properties of the traditional tools (e.g., geometry and material). Instead, we optimize the tools based on a characterization of the haptic feedback given by a recently proposed perceptual space of drawing tools [Piovarči et al 2018], which enables focusing on perceptually-relevant tool characteristics. Second, we employ a fabrication-in-the-loop approach that systematically explores the space of possible designs in a search for the optimal one.…”