Functional Workspace Optimization via Learning Personal Preferences from Virtual Experiences



Wei Liang1    Jingjing Liu1    Yining Lang1    Bing Ning2    Lap-Fai Yu3

1Beijing Institute of Technology    2Beijing Institute of Fashion Technology    3George Mason University   


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Abstract

The functionality of a workspace is one of the most important considerations in both virtual world design and interior design. To offer appropriate functionality to the user, designers usually take some general rules into account, e.g., general workflow and average stature of users, which are summarized from the population statistics. Yet, such general rules cannot reflect the personal preferences of a single individual, which vary from person to person. In this paper, we intend to optimize a functional workspace according to the personal preferences of the specific individual who will use it. We come up with an approach to learn the individual personal preferences from his activities while using a virtual version of the workspace via virtual reality devices. Then, we construct a cost function, which incorporates personal preferences, spatial constraints, pose assessments, and visual field. At last, the cost function is optimized to achieve an optimal layout. To evaluate the approach, we experimented with different settings. The results of the user study show that the workspaces updated in this way better fit the users.


Publication

Functional Workspace Optimization via Learning Personal Preferences from Virtual Experiences
Wei Liang, Jingjing Liu, Yining Lang, Bing Ning, Lap-Fai Yu
IEEE Transactions on Visualization and Computer Graphics (Special Issue on IEEE Virtual Reality 2019)
Paper , Video

BibTex

@article{wei2019functional,
    title= {Functional Workspace Optimization via Learning Personal Preferences from Virtual Experiences},
    author = {Liang, Wei and Liu, Jingjing and Lang, yining and Ning, Bing and Yu, Lap-Fai},
    journal = {IEEE Transactions on Visualization and Computer Graphics},
    year = {2019}
    publisher = {IEEE}
}






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  • Media Computing and Intelligent Systems Lab


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