Detail-preserving paint modeling for 3D brushes

Abstract

Figure 1: Oil paint simulated by an implementation of the system presented in [Baxter et al. 2001] (left), by our method (middle) and by the commercial package ArtRage 3 (right). Compared with [Baxter et al. 2001], our technique produces more organic color streaks and much less color blurring. Compared with the ArtRage strokes, ours appear more natural since the ArtRage method of sweeping a 1D texture to simulate strokes give less variation and control than the 3D deformable brushes offered by our system. AbstractRecent years have witnessed significant advances in 3D brush modeling and simulation in digital paint tools. Compared with traditional 2D brushes, a 3D brush can be both more intuitive and more expressive by offering an experience closer to wielding a real, physical brush. To support popular media types such as oil and pastel, most previous 3D brush models have implemented paint smearing and mixing. This is generally accomplished by a simple repeated exchange of paint between the 3D brush and 2D canvas, with the paint picked up by the brush typically mapped directly onto the brush surface. In this paper we demonstrate that both repeated exchanges and direct mapping of paint onto brush surfaces are sub-optimal choices, leading to excessive loss of color detail and computational inefficiencies. We present new techniques to solve both problems, first by using a canvas snapshot buffer to prevent repeated paint exchange, and second by mapping brush paint onto a 2D, resolution-matched pickup map that sits underneath the brush, instead of mapping onto the 3D brush itself. Together, these act to minimize resampling artifacts, helping to preserve fine streaks and color details in strokes, while at the same time yielding improved efficiency by never sampling the brush more densely than necessary. We demonstrate the effectiveness of our method in a real-time paint system implemented on the GPU that simulates pastel and oil paint. Our method is simple and effective, and achieves a level of realism for these two media not attained by any previous work.

Codimensional non-Newtonian fluids

“…One major application area of fluid simulation in high codimensions is painting [Baxter et al 2001;Baxter et al 2004]. Much research has been devoted to creating systems that simulate paint phenomena that occur in high codimensions, such as paint jets [Lee et al 2006], streaks [Chu et al 2010;DiVerdi et al 2013], and color dispersion [Chu and Tai 2005]. These systems produce realistic painting results and support real-time user interaction.…”

Enhancing naturalness of pen-and-tablet drawing through context sensing

“…HandSCAPE [8] is an orientationaware digital tape measure that uses embedded sensors to capture the linear measurements of physical workplaces. In the pen space, Project Gustav [3] attempts to mimic the paintbrush experience by constructing virtual brush strokes in 3D using pen orientation information and physics simulation. This paper differs from these works in that we aim to understand the higher-level contexts of drawing determined by the user, instead of reproducing lower-level physicality.…”

Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting

“…Several previous works modeled the brush shapes, its dynamics, and its interaction with the paper, as well as simulated the ink dispersion and absorption by a paper, e.g, the hairy brushes [18] and the physics-based models [4], [5], [16].…”

“…Some of them use graphics processing units for speedup [4]. However, due to over-simplification, none of these methods has been able to simulate certain special brush strokes such as impasto created with paint knives.…”