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

Fidelity vs. Simplicity: a Global Approach to Line Drawing Vectorization

International audienceVector drawing is a popular representation in graphic design because of the precision, compactness and editability offered by parametric curves. However, prior work on line drawing vectorization focused solely on faithfully capturing input bitmaps, and largely overlooked the problem of producing a compact and editable curve network. As a result, existing algorithms tend to produce overly-complex drawings composed of many short curves and control points, especially in the presence of thick or sketchy lines that yield spurious curves at junctions. We propose the first vectorization algorithm that explicitly balances fidelity to the input bitmap with simplicity of the output, as measured by the number of curves and their degree. By casting this trade-off as a global optimization, our algorithm generates few yet accurate curves, and also disambiguates curve topology at junctions by favoring the simplest interpretations overall. We demonstrate the robustness of our algorithm on a variety of drawings, sketchy cartoons and rough design sketches

A complete hand-drawn sketch vectorization framework

Vectorizing hand-drawn sketches is a challenging task, which is of paramount importance for creating CAD vectorized versions for the fashion and creative workflows. This paper proposes a complete framework that automatically transforms noisy and complex hand-drawn sketches with different stroke types in a precise, reliable and highly-simplified vectorized model. The proposed framework includes a novel line extraction algorithm based on a multi-resolution application of Pearson's cross correlation and a new unbiased thinning algorithm that can get rid of scribbles and variable-width strokes to obtain clean 1-pixel lines. Other contributions include variants of pruning, merging and edge linking procedures to post-process the obtained paths. Finally, a modification of the original Schneider's vectorization algorithm is designed to obtain fewer control points in the resulting Bezier splines. All the proposed steps of the framework have been extensively tested and compared with state-of-the-art algorithms, showing (both qualitatively and quantitatively) its outperformance

Diachronic Reconstruction and Visualization of Lost Cultural Heritage Sites

25 p.Cultural heritage (CH) documentation is essential for the study and promotion of CH assets/sites, and provides a way of transmitting knowledge about heritage to future generations. The integration of the fourth dimension into geospatial datasets enables generating a diachronic model of CH elements, namely, a set of three-dimensional (3D) models to represent their evolution in various historical phases. The enhanced four-dimensional (4D) modeling (3D plus time) pursues a better understanding of the CH scenario, enriching historical hypotheses as well as contributing to the conservation and decision-making process. Although new geomatic techniques have reduced the amount of fieldwork, when put together, the geometric and temporal dimensions imply the interpretation of heterogeneous historical information sources and their integration. However, this situation could reach a critical point when the study elements are no longer present. The main challenge is to harmonize the different historical and archaeological data sources that are available with the current remains in order to graphically rebuild and model the lost CH assets with a high degree of reliability. Moreover, 4D web visualization is a great way to disclose the CH information and cultural identity. Additionally, it will serve as a basis to perform simulations of possible future risks or changes that can happen during planned or hypothetical restoration processes. This paper aims to examine the study case of a diachronic reconstruction by means of a mobile laser system (MLS) and reverse modeling techniques for a lost urban CH element: the citadel or Alcázar gate of Ávila. Within this aim, the final model is evaluated in terms of the consistency of the historical sources to assess its suitability considering the constructive interpretations that are required to integrate heterogenous data sources. Moreover, geometric modeling is evaluated regarding the current remains and its surroundings. Finally, a web 4D viewer is presented for its dissemination and publicity. This paper is an extended and improved version of our paper that was published in the 2018 ISPRS Technical Commission II Symposium, Riva del Garda, Italy, 3–7 June 2018.S

ARCHITECTURE ESTIMATION FROM SPARSE IMAGES USING GRAMMATICAL SHAPE PRIORS FOR CULTURAL HERITAGE

The estimation and reconstruction of 3D architectural structures is of great in- terest in computer vision, as well as cultural heritage. This dissertation proposes a novel approach to solve the di??cult problem of estimating architectural structures from sparse images and e??ciently generating 3D models from estimation results for cultural heritage. This approach takes as input one plan drawing image and a few fac¸ade images, and provides as output the volumetric 3D models which represent the structures in the sparse images. Support of this research goal has motivated new investigations in underlying structure estimation problems including detecting structural feature points in 2D images, decomposing plan drawings into semantically meaningful shapes for medieval castles, estimating rectangular and Gothic fac¸ades using shape priors, and estimating complete 3D models for architectural structures using a novel volumetric shape grammar. Major outstanding challenges in each of these topic areas are addressed resulting in contributions to current state-of-the-art as it applied to these di??cult problems

Computational Aspects of Heat Transfer in Structures

Techniques for the computation of heat transfer and associated phenomena in complex structures are examined with an emphasis on reentry flight vehicle structures. Analysis methods, computer programs, thermal analysis of large space structures and high speed vehicles, and the impact of computer systems are addressed