ICAT-EGVE2020

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https://diglib.eg.org/handle/10.2312/2632961

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Browsing ICAT-EGVE2020 by Subject "Computing methodologies"

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    FrictGAN: Frictional Signal Generation from Fabric Texture Images using Generative Adversarial Network
    (The Eurographics Association, 2020) Cai, Shaoyu; Ban, Yuki; Narumi, Takuji; Zhu, Kening; Argelaguet, Ferran and McMahan, Ryan and Sugimoto, Maki
    The electrostatic tactile display could render the tactile feeling of different haptic texture surfaces by generating the frictional force through voltage modulation when a finger is sliding on the display surface. However, it is challenging to prepare and fine-tune the appropriate frictional signals for haptic design and texture simulation. We present FrictGAN, a deep-learningbased framework to synthesize frictional signals for electrostatic tactile displays from fabric texture images. Leveraging GANs (Generative Adversarial Networks), FrictGAN could generate the displacement-series data of frictional coefficients for the electrostatic tactile display to simulate the tactile feedback of fabric material. Our preliminary experimental results showed that FrictGAN could achieve considerable performance on frictional signal generation based on the input images of fabric textures.
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    A Systematic Literature Review of Embodied Augmented Reality Agents in Head-Mounted Display Environments
    (The Eurographics Association, 2020) Norouzi, Nahal; Kim, Kangsoo; Bruder, Gerd; Erickson, Austin; Choudhary, Zubin; Li, Yifan; Welch, Greg; Argelaguet, Ferran and McMahan, Ryan and Sugimoto, Maki
    Embodied agents, i.e., computer-controlled characters, have proven useful for various applications across a multitude of display setups and modalities. While most traditional work focused on embodied agents presented on a screen or projector, and a growing number of works are focusing on agents in virtual reality, a comparatively small number of publications looked at such agents in augmented reality (AR). Such AR agents, specifically when using see-through head-mounted displays (HMDs) as the display medium, show multiple critical differences to other forms of agents, including their appearances, behaviors, and physical-virtual interactivity. Due to the unique challenges in this specific field, and due to the comparatively limited attention by the research community so far, we believe that it is important to map the field to understand the current trends, challenges, and future research. In this paper, we present a systematic review of the research performed on interactive, embodied AR agents using HMDs. Starting with 1261 broadly related papers, we conducted an in-depth review of 50 directly related papers from 2000 to 2020, focusing on papers that reported on user studies aiming to improve our understanding of interactive agents in AR HMD environments or their utilization in specific applications. We identified common research and application areas of AR agents through a structured iterative process, present research trends, and gaps, and share insights on future directions.