True2Form

Xu, Baoxuan; Chang, W.S.C.; Sheffer, Alla; Bousseau, Adrien; McCrae, James; Singh, Karan

doi:10.1145/2601097.2601128

Cited by 123 publications

(17 citation statements)

References 37 publications

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“…This new goal brings us to prior work in the area of sketch-based modeling, where a number of methods relied on the analysis of complex sketches to infer a 3D shape or a 2.5D high relief from a single sketch, e.g., [Cordier and Seo 2007;Karpenko and Hughes 2006;Xu et al 2014;Yeh et al 2017]. Most methods in the area built on a priori knowledge (or contextual information) in order to resolve ambiguities -such as requiring exact symmetric shapes [Cordier et al 2011], being restricted to garments [Turquin et al 2007] or to side views of animals [Entem et al 2014] or requesting 3D information such as a 3D skeleton from the user [Bessmeltsev et al 2015].…”

Section: Related Workmentioning

confidence: 99%

Structuring and layering contour drawings of organic shapes

Parakkat

Cani

Barthe

2018

Proceedings of the Joint Symposium on Computational Aesthetics and Sketch-Based Interfaces and Modeling and Non-Photorealistic

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Complex vector drawings serve as convenient and expressive visual representations, but they remain difficult to edit or manipulate. For clean-line vector drawings of smooth organic shapes, we describe a method to automatically extract a layered structure for the drawn object from the current or nearby viewpoints. The layers correspond to salient regions of the drawing, which are often naturally associated to 'parts' of the underlying shape. We present a method that automatically extracts salient structure, organized as parts with relative depth orderings, from clean-line vector drawings of smooth organic shapes. Our method handles drawings that contain complex internal contours with T-junctions indicative of occlusions, as well as internal curves that may either be expressive strokes or substructures. To extract the structure, we introduce a new partaware metric for complex 2D drawings, the radial variation metric, which is used to identify salient sub-parts. These sub-parts are then considered in a priority-ordered fashion, which enables us to identify and recursively process new shape parts while keeping track of Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. Expressive '18, August 17-19, 2018, Victoria, BC, Canada © 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-1-4503-5892-7/18/08. . . $15.00 https://doi.org/10.1145/3229147.3229155 their relative depth ordering. The output is represented in terms of scalable vector graphics layers, thereby enabling meaningful editing and manipulation. We evaluate the method on multiple input drawings and show that the structure we compute is convenient for subsequent posing and animation from nearby viewpoints.

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Section: Related Workmentioning

confidence: 99%

Structuring and layering contour drawings of organic shapes

Parakkat

Cani

Barthe

2018

Proceedings of the Joint Symposium on Computational Aesthetics and Sketch-Based Interfaces and Modeling and Non-Photorealistic

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“…Photographs have also been used in conjunction with sketches to disambiguate form [Lau et al 2010]. Xu et al [2014] infer 3D curve networks from a given 2D sketched design by assuming implied regularities, such as planarity, curvature, symmetry, and parallelism. In contrast, we target our system at freeform space curves which may or may not directly participate in defining some underlying surface.…”

Section: Related Workmentioning

confidence: 99%

“…One way to make drawing in 3D easier is to impose assumptions about the underlying form, such as regularity, symmetry, planarity, and orthogonality [Bae et al 2008;Kara and Shimada 2007;Schmidt et al 2009b;Xu et al 2014]. While these approaches work well in practice they are often restricted to a certain class of shapes, which may limit artistic expressiveness.…”

Section: Introductionmentioning

confidence: 99%

Skippy

et al. 2017

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We introduce Skippy, a novel algorithm for 3D interactive curve modeling from a single view. While positing curves in space can be a tedious task, our rapid sketching algorithm allows users to draw curves in and around existing geometry in a controllable manner. The key insight behind our system is to automatically infer the 3D curve coordinates by enumerating a large set of potential curve trajectories. More specifically, we partition 2D strokes into continuous segments that land both on and off the geometry, duplicating segments that could be placed in front or behind, to form a directed graph. We use distance fields to estimate 3D coordinates for our curve segments and solve for an optimally smooth path that follows the curvature of the scene geometry while avoiding intersections. Using our curve design framework we present a collection of novel editing operations allowing artists to rapidly explore and refine the combinatorial space of solutions. Furthermore, we include the quick placement of transient geometry to aid in guiding the 3D curve. Finally we demonstrate our interactive design curve system on a variety of applications including geometric modeling, and camera motion path planning.

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“…A large body of research deals with interpreting a collection of 2D strokes as a 3D object represented using a network of 3D curves. Tools such as ILoveSketch [3], True2Form [46], and analytic 3D drawing [1], infer 3D from 2D using a mix of regularity constraints that define geometric relationships between the curves in 3D. Curves are often inferred as cleanly drawn 2D strokes projected onto appropriate 3D planes in these approaches.…”

Section: Sketching In 3dmentioning

confidence: 99%

Storeoboard

Henrikson

Araújo

Chevalier

et al. 2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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Figure 1. Storeoboard augments sketch-based storyboards with stereoscopic 3D planes for a fluid and flexible authoring of stereoscopic storyboards. -Stereo images in this paper are best viewed with red/cyan anaglyph glasses - ABSTRACTWe present Storeoboard, a system for stereo-cinematic conceptualization, via storyboard sketching directly in stereo. The resurgence of stereoscopic media has motivated filmmakers to evolve a new stereo-cinematic vocabulary, as many principles for stereo 3D film are unique. Concepts like plane separation, parallax position, and depth budgets are missing from early planning due to the 2D nature of existing storyboards. Storeoboard is the first of its kind, allowing filmmakers to explore, experiment and conceptualize ideas in stereo early in the film pipeline, develop new stereo-cinematic constructs and foresee potential difficulties. Storeoboard is the design outcome of interviews and field work with directors, stereographers, and storyboard artists. We present our design guidelines and implementation of a tool combining stereo-sketching, depth manipulations and storyboard features into a coherent and novel workflow. We report on feedback from storyboard artists, industry professionals and the director of a live action, feature film on which Storeoboard was deployed.

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True2Form

Cited by 123 publications

References 37 publications

Structuring and layering contour drawings of organic shapes

Structuring and layering contour drawings of organic shapes

Skippy

Storeoboard

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