Showing papers on "Procedural texture published in 2021"
TL;DR: An edge-based procedural texture (EBPT) as mentioned in this paper is a procedural model for semi-stochastic texture generation, which focuses on edges as the visually salient features extracted from the input image and organizes into groups with clearly established spatial properties.
Abstract: We introduce an edge-based procedural texture (EBPT), a procedural model for semi-stochastic texture generation. EBPT quickly generates large textures from a small input image. EBPT focuses on edges as the visually salient features extracted from the input image and organizes into groups with clearly established spatial properties. EBPT allows the users to interactively or automatically design new textures by utilizing the edge groups. The output texture can be significantly larger than the input, and EBPT does not need multiple textures to mimic the input. EBPT-based texture synthesis consists of two major steps, input analysis and texture synthesis. The input analysis stage extracts edges, builds the edge groups, and stores procedural properties. The texture synthesis stage distributes edge groups with affine transformation. This step can be done interactively or automatically using the procedural model. Then, it generates the output using edge group-based seamless image cloning. We demonstrate our method on various semi-stochastic inputs. With just a few input parameters defining the final structure, our method can analyze the input size of $$512\times {512}$$
in 0.7 s and synthesize the output texture of $$2048\times {2048}$$
pixels in 0.5 s.
4 citations
6 Sep 2021
TL;DR: In this paper, the authors investigate the extent to which animated procedural texture patterns can be used to support the representation of changes in 2.5D treemaps and conclude which of the patterns are more likely to increase effectiveness and correctness of the change mapping.
Abstract: This work investigates the extent to which animated procedural texture patterns can be used to support the representation of changes in 2.5D treemaps. Changes in height, color, and area of individual nodes can easily be visualized using animated transitions. Especially for changes in the color attribute, plain animated transitions are not able to directly communicate the direction of change itself. We show how procedural texture patterns can be superimposed to the color mapping and support transitions. To this end, we discuss qualitative properties of each pattern, demonstrate their ability to communicate change direction both with and without animation, and conclude which of the patterns are more likely to increase effectiveness and correctness of the change mapping in 2.5D treemaps.
3 citations
TL;DR: Wang et al. as mentioned in this paper used a two-dimensional cellular automaton to simulate a brushstroke model with ink and wash style, and outlines are drawn along the path of the brushstroke to obtain an effect close to the artistic style of ink and washing painting.
Abstract: This paper starts with the study of realistic three-dimensional models, from the two aspects of ink art style simulation model and three-dimensional display technology, explores the three-dimensional display model of three-dimensional model ink style, and conducts experiments through the software development platform and auxiliary software. The feasibility of the model is verified. Aiming at the problem of real-time rendering of large-scale 3D scenes in the model, efficient visibility rejection method and a multiresolution fast rendering method were designed to realize the rapid construction and rendering of ink art 3D virtual reality scenes in a big data environment. A two-dimensional cellular automaton is used to simulate a brushstroke model with ink and wash style, and outlines are drawn along the path of the brushstroke to obtain an effect close to the artistic style of ink and wash painting. Set the surface of the model with ink style brushstroke texture patterns, refer to the depth map, normal map, and curvature map information of the model, and simulate the drawing effect of the method by procedural texture mapping. Example verification shows that the rapid visualization analysis model of ink art big data designed in this paper is in line with the prediction requirements of ink art big data three-dimensional display indicators. The fast visibility removal method is used to deal with large-scale three-dimensional ink art in a big data environment. High efficiency is achieved in virtual reality scenes, and the multiresolution fast rendering method better maintains the appearance of the prediction model without major deformation.
2 citations
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TL;DR: In this paper, the authors use differentiable programming to train a generative process, parameterised by a recurrent Neural Cellular Automata (NCA) rule, for procedural texture synthesis using highly compact models.
Abstract: We study the problem of example-based procedural texture synthesis using highly compact models. Given a sample image, we use differentiable programming to train a generative process, parameterised by a recurrent Neural Cellular Automata (NCA) rule. Contrary to the common belief that neural networks should be significantly over-parameterised, we demonstrate that our model architecture and training procedure allows for representing complex texture patterns using just a few hundred learned parameters, making their expressivity comparable to hand-engineered procedural texture generating programs. The smallest models from the proposed $\mu$NCA family scale down to 68 parameters. When using quantisation to one byte per parameter, proposed models can be shrunk to a size range between 588 and 68 bytes. Implementation of a texture generator that uses these parameters to produce images is possible with just a few lines of GLSL or C code.