Dream Lens

Matejka, Justin; Glueck, Michael; Bradner, Erin; Hashemi, Ali; Grossman, Tovi; Fitzmaurice, George

doi:10.1145/3173574.3173943

Cited by 92 publications

(26 citation statements)

References 30 publications

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“…Capturing aspects of the way products behave and fit into the world around them is seen as a means to consider new capacities to innovate and to support users. Whilst there are already good examples of datadriven design optimisation systems [36], HCI research that actively challenges the term 'data-driven design' and explores alternative human-data interactions to support early-stage design may prove valuable in this space. Against a tendency to rely on more data and more computation to solve problems, we would question how much data is needed to drive the process, rather than being seen as a supporting material.…”

Section: Expanding Notions Of Data-driven Designmentioning

confidence: 99%

Exploring The Future of Data-Driven Product Design

Gorkovenko

Burnett

Thorp

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Connected devices present new opportunities to advance design through data collection in the wild, similar to the way digital services evolve through analytics. However, it is still unclear how live data transmitted by connected devices informs the design of these products, going beyond performance optimisation to support creative practices. Design can be enriched by data captured by connected devices, from usage logs to environmental sensors, and data about the devices and people around them. Through a series of workshops, this paper contributes industry and academia perspectives on the future of data-driven product design. We highlight HCI challenges, issues and implications, including sensemaking and the generation of design insight. We further challenge current notions of data-driven design and envision ways in which future HCI research can develop ways to work with data in the design process in a connected, rich, human manner.

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Section: Expanding Notions Of Data-driven Designmentioning

confidence: 99%

Exploring The Future of Data-Driven Product Design

Gorkovenko

Burnett

Thorp

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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“…Situated among this literature, we believe that SimuLearn's speed and accuracy will allow available CAD tools to become more augmentative and effective in forward and inverse design tasks. Taking inspiration from Dream Lens [25], we also believe that SimuLearn can support generative tasks and allow users and computers to co-design morphing materials.…”

Section: Functional Simulation In Hcimentioning

confidence: 99%

SimuLearn

Yang

Qian

Liu

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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Morphing materials allow us to create new modalities of interaction and fabrication by leveraging the materials' dynamic behaviors. Yet, despite the ongoing rapid growth of computational tools within this realm, current developments are bottlenecked by the lack of an effective simulation method. As a result, existing design tools must trade-off between speed and accuracy to support a real-time interactive design scenario. In response, we introduce SimuLearn, a data-driven method that combines finite element analysis and machine learning to create real-time (0.61 seconds) and truthful (97% accuracy) morphing material simulators. We use mesh-like 4D printed structures to contextualize this method and prototype design tools to exemplify the design workflows and spaces enabled by a fast and accurate simulation method. Situating this work among existing literature, we believe SimuLearn is a timely addition to the HCI CAD toolbox that can enable the proliferation of morphing materials.

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“…Autodesk's Project DreamCatcher takes a data-driven approach to generate hundreds of thousands of design options based on input functional requirements [47]. To explore the many generated design alternatives, DreamLens provides a visualization platform built for exploring large-scale design datasets [36]. DreamSketch allows a user to integrate generatively designed components with the workflow of sketching [24].…”

Section: Reality-based Design Toolsmentioning

confidence: 99%

Robiot: A Design Tool for Actuating Everyday Objects with Automatically Generated 3D Printable Mechanisms

Kim

Chen

2019

Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology

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D r a we r , o p e n. Figure 1. Robiot enables end-users to take a video and automatically generates mechanisms that can actuate everyday objects to perform simple physical tasks. All the four examples were designed by participants in our design session: lowering a lamp while busy soldering (a), waving a hand up to open a trashcan afar (c), setting up a scheduled house plant water spraying system (e), and asking the drawer to open itself when having no extra hand (g).

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Dream Lens

Cited by 92 publications

References 30 publications

Exploring The Future of Data-Driven Product Design

Exploring The Future of Data-Driven Product Design

SimuLearn

Robiot: A Design Tool for Actuating Everyday Objects with Automatically Generated 3D Printable Mechanisms

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