Vector Graphics Complexes

International audienceBasic topological modeling, such as the ability to have several faces share a common edge, has been largely absent from vector graphics. We introduce the vector graphics complex (VGC) as a simple data structure to support fundamental topological modeling operations for vector graphics illustrations. The VGC can represent any arbitrary non-manifold topology as an immersion in the plane, unlike planar maps which can only represent embeddings. This allows for the direct representation of incidence relationships between objects and can therefore more faithfully capture the intended semantics of many illustrations, while at the same time keeping the geometric flexibility of stacking-based systems. We describe and implement a set of topological editing operations for the VGC, including glue, unglue, cut, and uncut. Our system maintains a global stacking order for all faces, edges, and vertices without requiring that components of an object reside together on a single layer. This allows for the coordinated editing of shared vertices and edges even for objects that have components distributed across multiple layers. We introduce VGC-specific methods that are tailored towards quickly achieving desired stacking orders for faces, edges, and vertices

Importing Vector Graphics: The grImport Package for R

This article describes an approach to importing vector-based graphical images into statistical software as implemented in a package called grImport for the R statistical computing environment. This approach assumes that an original image can be transformed into a PostScript format (i.e., the original image is in a standard vector graphics format such as PostScript, PDF, or SVG). The grImport package consists of three components: a function for converting PostScript files to an R-specific XML format; a function for reading the XML format into special Picture objects in R; and functions for manipulating and drawing Picture objects. Several examples and applications are presented, including annotating a statistical plot with an imported logo and using imported images as plotting symbols.

Im2Vec: Synthesizing Vector Graphics without Vector Supervision

Vector graphics are widely used to represent fonts, logos, digital artworks, and graphic designs. But, while a vast body of work has focused on generative algorithms for raster images, only a handful of options exists for vector graphics. One can always rasterize the input graphic and resort to image-based generative approaches, but this negates the advantages of the vector representation. The current alternative is to use specialized models that require explicit supervision on the vector graphics representation at training time. This is not ideal because large-scale high quality vector-graphics datasets are difficult to obtain. Furthermore, the vector representation for a given design is not unique, so models that supervise on the vector representation are unnecessarily constrained. Instead, we propose a new neural network that can generate complex vector graphics with varying topologies, and only requires indirect supervision from readily-available raster training images (i.e., with no vector counterparts). To enable this, we use a differentiable rasterization pipeline that renders the generated vector shapes and composites them together onto a raster canvas. We demonstrate our method on a range of datasets, and provide comparison with state-of-the-art SVG-VAE and DeepSVG, both of which require explicit vector graphics supervision. Finally, we also demonstrate our approach on the MNIST dataset, for which no groundtruth vector representation is available. Source code, datasets, and more results are available at geometry.cs.ucl.ac.uk/projects/2021/Im2Vec

Mesh Colours for Gradient Meshes

We present an extension of the popular gradient mesh vector graphics primitive with the addition of mesh colours, aiming to reduce the mesh complexity needed to describe intricate colour gradients and textures. We present interesting applications to user-guided authoring of detailed vector graphics and image vectorisation

Interactive Visual Histories for Vector Graphics

Presentation and graphics software enables users to experiment with variations of illustrations. They can revisit recent editing operations using the ubiquitous undo command, but they are limited to sequential exploration. We propose a new interaction metaphor and visualization for operation history. While editing, a user can access a history mode in which actions are denoted by graphical depictions appearing on top of the document. Our work is inspired by the visual language of film storyboards and assembly instructions. Our storyboard provides an interactive visual history, summarizing the editing of a document or a selected object. Each view is composed of action depictions representing the userâ s editing actions and enables the user to consider the operation history in context rather than in a disconnected list view. This metaphor provides instant access to any past action and we demonstrate that this is an intuitive interface to a selective undo mechanism

An automatic marker for vector graphics drawing tasks

In recent years, the SVG file format has grown increasingly popular, largely due to its widespread adoption as the standard image format for vector graphics on the World Wide Web. However, vector graphics predate the modern Web, having served an important role in graphic and computer-aided design for decades prior to SVG's adoption as a web standard. Vector graphics are just as - if not more - relevant than ever today. As a result, training in vector graphics software, particularly in graphic and other creative design fields, forms an important part of the skills development necessary to enter the industry. This study explored the feasibility of a web application that can automatically mark/assess drawing tasks completed in popular vector graphics editors such as Adobe Illustrator, CorelDRAW, and Inkscape. This prototype has been developed using a collection of front-end and back-end web technologies, requiring that users need only a standards-compliant, modern web browser to submit tasks for assessment. Testing was carried out to assess how the application handled SVG markup produced by different users and vector graphics drawing software; and whether the assessment/scoring of submitted tasks was inline with that of a human marker. While some refinement is required, the application assessed six different tasks, submitted eleven times over by as many individuals, and for the greater part was successful in reporting scores in line with that of the researcher. As a prototype, serving as a proof of concept, the project proved the automatic marker a feasible concept. Exactly how marks should be assigned, for which criteria, and how much instruction should be provided are aspects for further study; along with support for curved path segments, and automatic task generation

VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models

Diffusion models have shown impressive results in text-to-image synthesis. Using massive datasets of captioned images, diffusion models learn to generate raster images of highly diverse objects and scenes. However, designers frequently use vector representations of images like Scalable Vector Graphics (SVGs) for digital icons or art. Vector graphics can be scaled to any size, and are compact. We show that a text-conditioned diffusion model trained on pixel representations of images can be used to generate SVG-exportable vector graphics. We do so without access to large datasets of captioned SVGs. By optimizing a differentiable vector graphics rasterizer, our method, VectorFusion, distills abstract semantic knowledge out of a pretrained diffusion model. Inspired by recent text-to-3D work, we learn an SVG consistent with a caption using Score Distillation Sampling. To accelerate generation and improve fidelity, VectorFusion also initializes from an image sample. Experiments show greater quality than prior work, and demonstrate a range of styles including pixel art and sketches. See our project webpage at https://ajayj.com/vectorfusion .Comment: Project webpage: https://ajayj.com/vectorfusio