Sequential line search for efficient visual design optimization by crowds

Koyama, Yuki; Sato, Issei; Sakamoto, Daisuke; Igarashi, Takeo

doi:10.1145/3072959.3073598

Cited by 64 publications

(80 citation statements)

References 33 publications

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“…Our fabrication-in-the-loop approach draws inspiration from active learning, where the learning process is enhanced by engaging users to label new data-points iteratively. Also here, surrogate models like Gaussian Processes are often used to generate the datasets presented to the participants [Akrour et al 2011;Dudley et al 2019;Koyama et al 2017]. Our method can be seen as an instance of active learning where the user queries are replaced by an oracle based on physical manufacturing and measurements of samples generated based on a surrogate model formulated using Gaussian Processes.…”

Section: Gaussian Processes and Active Learningmentioning

confidence: 99%

Fabrication-in-the-loop co-optimization of surfaces and styli for drawing haptics

et al. 2020

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Digital drawing tools are now standard in art and design workflows. These tools offer comfort, portability, and precision as well as native integration with digital-art workflows, software, and tools. At the same time, artists continue to work with long-standing, traditional drawing tools. One feature of traditional tools, well-appreciated by many artists and lacking in digital tools, is the specific and diverse range of haptic responses provided by them. Haptic feedback in traditional drawing tools provides unique, per-tool responses that help determine the precision and character of individual strokes. In this work, we address the problem of fabricating digital drawing tools that closely match the haptic feedback of their traditional counterparts. This requires the formulation and solution of a complex, co-optimization of both digital styli and the drawing surfaces they move upon. Here, a potentially direct formulation of this optimization with numerical simulation-in-the-loop is not yet viable. As in many complex design tasks, state-of-the-art methods do not currently offer predictive modeling at rates and scales that can account for the numerous, coupled, physical behaviors governing the haptics of styli and surfaces, nor for the limitations and uncertainties inherent in their fabrication processes. To address these challenges, we propose fabrication-in-the-loop optimization. Critical to making this strategy practical we construct our objective via a Gaussian Process that does not require computing derivatives with respect to design parameters. Our Gaussian Process surrogate model then provides both function estimates and confidence intervals that guide the efficient sampling of our design space. In turn, this sampling critically reduces the numbers of fabricated examples during exploration and automatically handles exploration-exploitation trade-offs. We apply our method to fabricate drawing tools that provide a wide range of haptic feedback, and demonstrate that they are often hard for users to distinguish from their traditional drawing-tool analogs.

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Section: Gaussian Processes and Active Learningmentioning

confidence: 99%

Fabrication-in-the-loop co-optimization of surfaces and styli for drawing haptics

et al. 2020

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“…Interactive parameter adjustments are common in various image manipulation tasks such as tweaking brightness and contrast for photo enhancement [Koyama et al 2017] or altering the parameters of tone mapping operators [Yoshida et al 2006]. However, it has not been feasible for experiments that require real-time renders of a 3D scene with advanced features such as sub-surface scattering and soft shadows until the development of modern graphics hardware and game engines.…”

Section: Related Workmentioning

confidence: 99%

Evalu-light: A practical approach for evaluating character lighting in real-time

Wisessing

McDonnell

2020

ACM Symposium on Applied Perception 2020

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The demand for cartoon animation content is on the rise, driven largely by social media, video conferencing and virtual reality. New content creation tools and the availability of open-source game engines allow characters to be animated by even novice creators (e.g., Loomie and Pinscreen for creating characters, Hyprface and Mo-tionX for facial motion capture, etc,). However, lighting of animated characters is a skilled art-form, and there is a lack of guidelines for lighting characters expressing emotion in an appealing manner. Recent perceptual research has attempted to provide user guidelines through rigorous and labor-intensive rating-scale experiments exploring many parameters of light [Wisessing et al. 2016, 2020]. We propose an alternative approach based on the method of adjustment, similar to common lighting tools in 3D content creation software, but with the power of modern real-time graphics. Our framework allows users to interactively adjust lighting parameters and instantly assess the results on the animated characters, instead of having to wait for the render to complete.

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“…Crowdsourcing has been employed at the intersection of interface design and Bayesian optimization to efficiently collect large numbers of user interactions. Koyama et al [11] demonstrate the potential of Bayesian optimization to assist with visual feature optimization. They decompose the higher-order optimization problem into one-dimensional line searches that can then be allocated to crowdworkers: crowdworkers select the point on the slider that yields the best visual appearance.…”

Section: Related Workmentioning

confidence: 99%

Crowdsourcing Interface Feature Design with Bayesian Optimization

Dudley

Jacques

Kristensson

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Designing novel interfaces is challenging. Designers typically rely on experience or subjective judgment in the absence of analytical or objective means for selecting interface parameters. We demonstrate Bayesian optimization as an efficient tool for objective interface feature refinement. Specifically, we show that crowdsourcing paired with Bayesian optimization can rapidly and effectively assist interface design across diverse deployment environments. Experiment 1 evaluates the approach on a familiar 2D interface design problem: a map search and review use case. Adding a degree of complexity, Experiment 2 extends Experiment 1 by switching the deployment environment to mobile-based virtual reality. The approach is then demonstrated as a case study for a fundamentally new and unfamiliar interaction design problem: web-based augmented reality. Finally, we show how the model generated as an outcome of the refinement process can be used for user simulation and queried to deliver various design insights. CCS CONCEPTS • Human-centered computing → Systems and tools for interaction design.

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Sequential line search for efficient visual design optimization by crowds

Cited by 64 publications

References 33 publications

Fabrication-in-the-loop co-optimization of surfaces and styli for drawing haptics

Fabrication-in-the-loop co-optimization of surfaces and styli for drawing haptics

Evalu-light: A practical approach for evaluating character lighting in real-time

Crowdsourcing Interface Feature Design with Bayesian Optimization

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