Aesthetic 3D model evolution

Learning Perceptual Aesthetics of 3-D Shapes From Multiple Views

“…Similarly, several works have also used visual characteristics such as color, light, line, shape, texture, and space and movement to relate to aesthetic experience. Abstract mathematical criteria such as entropy, complexity, and deviation from normality have been defined for aesthetic 3D designs [2]. Geometry characteristics such as lines, curvatures of free surfaces, their deviation ratio etc.…”

“…While the aesthetics of images has been extensively researched to date, work in 3D shape aesthetics is very limited, mainly exploring aesthetics with manually defined features such as curvature, symmetry and mathematical criteria such as bending energy and minimum variation surfaces [1], [2], [3]. In this work, we drop the notion of manually defined features as such features may be biased or incomplete to capture the aesthetics of a shape.…”

Special issue on affective design using big data

“…In line with the technologies of big data, affective computing has been examined over the past few years including product design (Ayas 2011; Koutsabasis and Istikopoulou 2013), fashion design (Sokolova and Fernández-Caballero 2015), web design (Koutsabasis and Istikopoulou 2013), media communication (Bergen and Ross 2013;Cao et al 2014), computer game (Yannakakis et al 2014), human computer interaction (Bakhtiyari, Taghavi, and Husain 2015;Park and Zhang 2015), service development (Hensher 2014; Morris and Guerra 2015) and urban landscape design. From the literature, a growing interest in mining multi-disciplinary affective data by both researchers and industry can be seen.…”

Use of GCF Aesthetic Measure in the Evolution of Landscape Designs

“…Likewise, authors (den Heijer and Eiben, 2011) used two well-known aesthetic measures, Bell Curve and GCF, as their fitness function to generate digital art of vector graphics. In the same process, authors (Bergen and Ross, 2012) used source image as an aesthetic fitness measure by reading its colour pixels to evolve an automatic vectorisation of that image.…”

“…In the evolution of art authors (den Heijer and Eiben, 2010); (den Heijer and Eiben, 2011), used four main various aesthetic measures: Benford's Law (Jolion, 2001), GCF, Information Theory and Ross & Ralf's Bell curve, to generate digital images automatically. Many authors have applied aesthetic measures to evolve various digital artefacts, 3D structures (Bergen, 2011) (Bergen and Ross, 2012), virtual creatures (Hornby and Pollack, 2001), evolutionary art (Bergen and Ross, 2011), 3D art (Pang and Hui, 2010), images (Romero et al, 2012) etc. in recent decades.…”