Real-time drawing assistance through crowdsourcing

Limpaecher, Alex; Feltman, Nicolas; Treuille, Adrien; Cohen, Michael F.

doi:10.1145/2461912.2462016

Cited by 59 publications

(35 citation statements)

References 20 publications

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“…Limpaecher et al [7] introduced a method to correct user input strokes by a consensus model collected from a crowdsourced drawing database. Su et al [8] presented the EZ-Sketching system that snaps user strokes to nearby edges using a novel three-level optimization.…”

Section: Related Workmentioning

confidence: 99%

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User-guided line abstraction using coherence and structure analysis

Tsai

Lee

et al. 2017

Comp. Visual Media

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Line drawing is a style of image abstraction where the perceptual content of the image is conveyed using distinct straight or curved lines. However, extracting semantically salient lines is not trivial and mastered only by skilled artists. While many parametric filters have successfully extracted accurate and coherent lines, their results are sensitive to parameter choice and easily lead to either an excessive or insufficient number of lines. In this work, we present an interactive system to generate concise line abstractions of arbitrary images via a few user specified strokes. Specifically, the user simply has to provide a few intuitive strokes on the input images, including tracing roughly along edges and scribbling on the region of interest, through a sketching interface. The system then automatically extracts lines that are long, coherent and share similar textural structures to form a corresponding highly detailed line drawing. We have tested our system with a wide variety of images. Our experimental results show that our system outperforms state-of-the-art techniques in terms of quality and efficiency.

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Section: Related Workmentioning

confidence: 99%

“…E(p) is the energy function defined in Eq. (7). E conflict is an energy term designed to prevent the same lines from being extracted for parallel neighbor strokes.…”

Section: Temporospatially Neighboring Strokesmentioning

confidence: 99%

User-guided line abstraction using coherence and structure analysis

Tsai

Lee

et al. 2017

Comp. Visual Media

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“…While computerized assistance in painting can be implemented in many ways, the prevailing approaches we recognize revolve around suggestion, as in ShadowDraw [Lee et al 2011] or [Eitz et al 2012]; tutorial systems such as [Iarussi et al 2013;Laviole and Hachet 2012] or SketchSketch Revolution [Fernquist et al 2011]; and beautification, as in [Zitnick 2013], [Limpaecher et al 2013], and HelpingHand [Lu et al 2012].…”

Section: Related Workmentioning

confidence: 99%

Augmented Airbrush for Computer Aided Painting (CAP)

et al. 2015

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We present an augmented airbrush that allows novices to experience the art of spray painting. Inspired by the thriving field of smart tools, our handheld device uses 6DOF tracking; augmentation of the airbrush trigger; and a specialized algorithm to restrict the application of paint to a preselected reference image. Our device acts both as a physical spraying device and as an intelligent assistive tool, providing simultaneous manual and computerized control. Unlike prior art, here the virtual simulation guides the physical rendering (inverse rendering), allowing for a new spray painting experience with singular physical results. We present our novel hardware design, control software, and a user study that verifies our research objectives.

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“…For example, portrait sketching can be assisted by analyzed face data [Dixon et al 2010] or crowd-sourced sketches [Limpaecher et al 2013]. To help users draw a larger collection of objects, Lee et al [2011] display shadows extracted from web images to interactively guide user progress, while Iarussi et al [2013] provide structural guides based on artistic principles.…”

Section: Previous Workmentioning

confidence: 99%

Autocomplete painting repetitions

Xing¹,

Chen

Wei³

2014

ACM Trans. Graph.

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The final outcome is shown in (c). Our system works for both individual strokes (e.g. the detailed hatches) and structured elements (e.g. the windows). Due to the spatial and temporal variations, these sketches are very difficult, if not impossible, to be generated via prior sequential clone systems. AbstractPainting is a major form of content creation, offering unlimited control and freedom of expression. However, it can involve tedious manual repetitions, such as stippling large regions or hatching complex contours. Thus, a central goal in digital painting research is to automate tedious repetitions while allowing user control. Existing methods impose a sequential order, in which a small exemplar is prepared and then cloned through additional gestures. Such sequential mode may break the continuous, spontaneous flow of painting. Moreover, it is more suitable for homogeneous areas than nuanced variations common in real paintings.We present an interactive digital painting system that autocompletes tedious repetitions while preserving nuanced variations and maintaining natural flows. Specifically, users paint as usual, while our system records and analyzes their workflows. When potential repetition is detected, our system predicts what the user might want to draw and offers auto-completes that adjust to the existing shape-color context. Our method eliminates the need for sequential creation-cloning and better adapts to the local painting contexts. Furthermore, users can choose to accept, ignore, or modify those predictions and thus maintain full control. Our method can be considered as the painting analogy of auto-completes in common typing and IDE systems. We demonstrate the quality and usability of our system through painting results and a pilot user study.

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Real-time drawing assistance through crowdsourcing

Cited by 59 publications

References 20 publications

User-guided line abstraction using coherence and structure analysis

User-guided line abstraction using coherence and structure analysis

Augmented Airbrush for Computer Aided Painting (CAP)

Autocomplete painting repetitions

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