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The ability to visually discern shape, form, and value is fundamental to observational drawing. However, developing this skill requires a drawer to perceive a “raw” version of the scene being drawn, often referred to as regaining the innocence of the eye. This work investigates how interactive projected light cues can be used to alter the perception of drawing objects and understand how users might control their own perception. We introduce an augmented reality system capable of dynamically projecting interactive light cues onto objects within a live drawing studio. We present the design of three cues that address challenging percepts for novice drawers: gauging proportion, discerning shape, and shifting visual attention. In a formal user study with novice and intermediate drawers, we evaluate the effectiveness of these cues in supporting observational drawing. We demonstrate how cues can be designed to correct subconscious errors and visually guide users in learning to draw.
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Shape Structuralizer: Design, Fabrication, and User-driven Iterative Refinement of 3D Mesh Models
Current Computer-Aided Design (CAD) tools lack proper support for guiding novice users towards designs ready for fabrication. We propose Shape Structuralizer (SS), an interactive design support system that repurposes surface models into structural constructions using rods and custom 3Dprinted joints. Shape Structuralizer embeds a recommendation system that computationally supports the user during design ideation by providing design suggestions on local refinements of the design. This strategy enables novice users to choose designs that both satisfy stress constraints as well as their personal design intent. The interactive guidance enables users to repurpose existing surface mesh models, analyze them in-situ for stress and displacement constraints, add movable joints to increase functionality, and attach a customized appearance. This also empowers novices to fabricate even complex constructs while ensuring structural soundness. We validate the Shape Structuralizer tool with a qualitative user study where we observed that even novice users were able to generate a large number of structurally safe designs for fabrication.
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- Award ID(s):
- 1632154
- PAR ID:
- 10112930
- Date Published:
- Journal Name:
- Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems Paper No. 663
- Page Range / eLocation ID:
- 1 to 12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3290605.3300893
