Design characterization and automatic identification of character lines in automotive field

Lucchi, Francesca; Persiani, Franco; Bagassi, Sara

doi:10.1007/s12008-014-0211-x

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…During exploratory sketching, it is common for the creator to focus on a few key leading curves [ 47 , 48 ] that capture a major portion of the shape’s character and the author’s intent, such as profiles, sections, and reflection lines [ 17 , 31 , 49 ]. For generating shape variations, creators can implement general transformation rules to the leading curves, such as outline transformation, structure transformation, substitution, addition, deletion, cutting, and view change [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

The perceived value of human-AI collaboration in early shape exploration: An exploratory assessment

Arias-Rosales

2022

PLoS ONE

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As a vital element of early shape exploration, divergence can be time-consuming and challenging, with iterative cycles where idea fixation and creative blocks must be overcome for fuzzy ideas to be fully expanded and understood. Despite interesting tools that have been developed for this purpose, some important challenges remain, as it appears that many designers still prefer simple freehand sketching and tend to defer the use of computational tools to later stages. This work presents an exploratory assessment of the perceived value of a new tool, Shapi, developed to assist early shape exploration by addressing some of the pitfalls reported in the literature. Shapi is envisioned as an autonomous assistant that provides local and global shape variations in the form of rough sketches based on an initial human sketch and interactive cycles. These shape variations are What-If scenarios and cognitive facilitators that may spark new ideas or enable a deeper understanding of the shape and the identification of interesting patterns. Shapi’s capabilities are explored in a diverse set of case studies with different purposes: nine implementations in industrial design, three in graphic design, and five with open-ended artistic purposes. These implementations are then used in a survey about initial perceived value in which the majority gave high ratings in terms of exploration (75.5% ≥ 4 out of 5), interpretation (83.7% ≥ 4), adaptation (77.6% ≥ 4), value (73.5% ≥ 4), creativity (69.4% ≥ 4), and general interest in the tool (79.6% ≥ 4). This work brings insight into promising functionalities, opportunities, and risks in the intersection between artificial intelligence, design, and art.

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