Globally Consistent Wrinkle-Aware Shading of Line Drawings

Abstract

Shading is a tedious process for artists involved in 2D cartoon and manga production given the volume of contents that the artists have to prepare regularly over tight schedule. While we can automate shading production with the presence of geometry, it is impractical for artists to model the geometry for every single drawing. In this work, we aim to automate shading generation by analyzing the local shapes, connections, and spatial arrangement of wrinkle strokes in a clean line drawing. By this, artists can focus more on the design rather than the tedious manual editing work, and experiment with different shading effects under different conditions. To achieve this, we have made three key technical contributions. First, we model five perceptual cues by exploring relevant psychological principles to estimate the local depth profile around strokes. Second, we formulate stroke interpretation as a global optimization model that simultaneously balances different interpretations suggested by the perceptual cues and minimizes the interpretation discrepancy. Lastly, we develop a wrinkle-aware inflation method to generate a height field for the surface to support the shading region computation. In particular, we enable the generation of two commonly-used shading styles: 3D-like soft shading and manga-style flat shading.

SKED: Sketch-guided Text-based 3D Editing

“…Starting with Teddy [20], early works focused on converting scribbles of 2D contours to intermediate forms such as closed polylines [26] or implicit functions [25,64,1,57,6]. Since lifting a single-view sketch to 3D is an under-constrained problem, additional constraints are usually introduced, such as correlating the inflated thickness with chordal axis depth of curved shapes [20], inferring shape and depth from user annotations [57,26,16,49,7,79,33,22], using existing reference models like human figures [69], and solving a system based on user constraints [46,24,63,13,12]. More recently, data-driven approaches were suggested to lift and reconstruct objects from multiview sketches with Conv-nets [39,11,34].…”

SRNPD: Spatial rendering network for pencil drawing stylization

“…Since the geometry information of stroke images mainly comes from the inner strokes, we focus on the analysis of inner strokes. Inspired by the work of Jayaraman et al, we design a simplified stroke model for the interior strokes. With this model, we can estimate the perceptual information and construct partial geometry for constraining the stroke shading procedure.…”

“…We propose to generate stroke images with a single‐shot bottom‐up (SSBU) neural network at different layers of the Gaussian image pyramid for simulating the coarse‐to‐fine contour drawing procedure of artists, so that our results can well exhibit the inner structure of the original image. Jayaraman et al considered that strokes can suggest certain sparse cues about the local shape or depth profile of the object in the drawing. We analyze four main perceptual cues, namely, T‐junction, convexity, continuity, and proximity, of the stroke images and interpret the geometric meaning of strokes based on psychological principles.…”