Axial Generation: A Concretism-Inspired Method for Synthesizing Highly Varied Artworks

Easton, Edward; Ekárt, Anikó; Bernardet, Ulysses

doi:10.1007/978-3-030-72914-1_8

Cited by 2 publications

(5 citation statements)

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“…The extensions to the AGP presented in this paper all have a single focus, to improve the versatility of the Axial Generation Process, based on the results presented in [17]. The process itself applies the Concretism style to constrain the search space to improve its ability to generate interesting and varied content.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Abstract art has numerous sub-genres contained within it. During the process of creating the system [17], one of the sub-styles nicely described the content which was being created: Concretism. As defined by Tate: "Concrete art is abstract art that is entirely free of any basis in observed reality and that has no symbolic meaning" [42], Concretism was introduced in 1930 by Theo van Doesburg who released the Manifesto of Concrete Art [45], outlining the vision for Concrete art, through six well-defined tenets describing how and why Concrete art should be created.…”

Section: Choosing a Stylementioning

confidence: 99%

“…The base process [17] serves as a proof-of-concept, showing that the method can generate a wide range of items, both in 2D and 3D. The base process provides many areas which can be extended to further increase the range and diversity of items that can be generated allowing a wider range of measures to be used to assess the created content while keeping within the bounds of Concretism.…”

Section: Axial Generation Processmentioning

confidence: 99%

“…For 2D content, the rendering process works in a similar way to creating 2D art, where a 3D object is abstracted down onto a 2D plane. The base process only allowed this flattening to happen from the top-down view of the items, with the size of the items determined by their distance from the canvas, referred to as perspective-driven flattening [17].…”

Section: Rendering the Items From Multiple Viewpointsmentioning

confidence: 99%

“…This paper extends the versatile Axial Generation Process (AGP), first introduced in [17]. This system produces artwork within the Concretism style, either abstract images in two dimensions or digital sculptures in three dimensions.…”

Section: Introductionmentioning

confidence: 99%

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Axial Generation: Mixing Colour and Shapes to Automatically Form Diverse Digital Sculptures

2022

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Automated computer generation of aesthetically pleasing artwork has been the subject of research for several decades. The unsolved problem of interest is how to please any audience without requiring too much of their involvement in the process of creation. Two-dimensional pictures have received a lot of attention; however, 3D artwork has remained relatively unexplored. This paper showcases an extended version of the Axial Generation Process (AGP), a versatile generation algorithm that can create both 2D and 3D items within the Concretism art style. The extensions presented here include calculating colour values for the artwork, increasing the range of forms that can be created through dynamic sizing of shapes and including more primitive shape types, finally, 2D items can be created from multiple viewpoints. Both 2D and 3D items generated through the AGP were evaluated against a set of formal aesthetic measures and compared against two established generation systems, one based on manipulating pixels/voxels and another tracking the path of particles through 2D and 3D space. This initial evaluation shows that the process is capable of generating visually varied items which exhibit a generally diverse range of values across the measures used, in both two and three dimensions. Comparatively, against the established generation processes, the AGP shows a good balance of performance and ability to create complex and visually varied items.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Choosing a Stylementioning

confidence: 99%

Section: Axial Generation Processmentioning

confidence: 99%

Section: Rendering the Items From Multiple Viewpointsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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