<commit_before>---
layout: archive
title: "Publication"
permalink: /publications/hapticsTexture.html
author_profile: true
---


<style type="text/css">
    @import url("./web.css");
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<div class="title_section">
    <div style="text-align:center;">
        <h1 class="papertitle">Preference-Driven Texture Modeling Through Interactive Generation and Search</h1>
        <div class="authors">IEEE Transactions on Haptics.</div>
        <div class="authors"><NAME>, <NAME>, <NAME>, <NAME>, <NAME>.</div>
        <div class="paper_link">
            <a href="/publications/papers/hapticsTexture.pdf" style="color:white"> [paper] </a>
        </div>
    </div>
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</span>  
<div style="text-align: justify;font-size:15px">
    <div style="float:left; clear: both;" align="center">
        <img src="https://drive.google.com/file/d/1oh9m85yTzEs7eYFT5jbfgvx657LaPQ_v" width="250" alt="" hspace="8"><br/> 
<!--         <iframe src="https://drive.google.com/uc?id=1MrH_5MZ4BsnrnRBPJmjzYyRNEx8Py5Gj" width="250" height="100" style="border: none"></iframe> -->
</div>
<p class="abstract">
<b>Abstract</b>: Data-driven texture modeling and rendering has pushed the limit of realism in haptics. 
  However, the lack of haptic texture databases, difficulties of model interpolation and expansion, 
  and the complexity of real textures prevent data-driven methods from capturing a large variety of textures and from customizing 
  models to suit specific output hardware or user needs. This work proposes an interactive texture generation and search framework driven by user input. 
  We design a GAN-based texture model generator, which can create a wide range of texture models using Auto-Regressive processes. 
  Our interactive texture search method, which we call preference-driven, follows an evolutionary strategy given guidance from user's preferred feedback 
  within a set of generated texture models. We implemented this framework on a 3D haptic device and conducted a two-phase user study to evaluate the 
  efficiency and accuracy of our method for previously unmodeled textures. The results showed that by comparing the feel of real and generated virtual 
  textures, users can follow an evolutionary process to efficiently find a virtual texture model that matched or exceeded the realism of a data-driven 
  model. 
  Furthermore, for 4 out of 5 real textures, 80% of the preference-driven models from participants were rated comparable to the data-driven models.
</p>
<br/>
</div>


<div class="w-container">
        <p class="text_italic">Our results: </p>
<!--    <video width="100%" height="100%" source="" src="https://www.youtube.com/watch?v=8-nCkeeR6o0" type="video/mp4" loop="true" autoplay="autoplay" controls muted></video> -->
<!--    <iframe src="https://drive.google.com/file/d/1QgW2gofJOiJHRRNV7t2A1nDBHfiV9a3e/preview" style=" width: 100%; height: 100%; border: none;"></iframe> --><commit_msg>Update hapticsTexture.html<commit_after>---
layout: archive
title: "Publication"
permalink: /publications/hapticsTexture.html
author_profile: true
---


<style type="text/css">
    @import url("./web.css");
</style>

<div class="title_section">
    <div style="text-align:center;">
        <h1 class="papertitle">Preference-Driven Texture Modeling Through Interactive Generation and Search</h1>
        <div class="authors">IEEE Transactions on Haptics.</div>
        <div class="authors"><NAME>, <NAME>, <NAME>, <NAME>, <NAME>.</div>
        <div class="paper_link">
            <a href="/publications/papers/hapticsTexture.pdf" style="color:white"> [paper] </a>
        </div>
    </div>
</div>


</span>  
<div style="text-align: justify;font-size:15px">
    <div style="float:left; clear: both;" align="center">
        <img src="https://drive.google.com/uc?id=1oh9m85yTzEs7eYFT5jbfgvx657LaPQ_v" width="250" alt="" hspace="8"><br/> 
<!--         <iframe src="https://drive.google.com/uc?id=1oh9m85yTzEs7eYFT5jbfgvx657LaPQ_v" width="250" height="100" style="border: none"></iframe> -->
</div>
<p class="abstract">
<b>Abstract</b>: Data-driven texture modeling and rendering has pushed the limit of realism in haptics. 
  However, the lack of haptic texture databases, difficulties of model interpolation and expansion, 
  and the complexity of real textures prevent data-driven methods from capturing a large variety of textures and from customizing 
  models to suit specific output hardware or user needs. This work proposes an interactive texture generation and search framework driven by user input. 
  We design a GAN-based texture model generator, which can create a wide range of texture models using Auto-Regressive processes. 
  Our interactive texture search method, which we call preference-driven, follows an evolutionary strategy given guidance from user's preferred feedback 
  within a set of generated texture models. We implemented this framework on a 3D haptic device and conducted a two-phase user study to evaluate the 
  efficiency and accuracy of our method for previously unmodeled textures. The results showed that by comparing the feel of real and generated virtual 
  textures, users can follow an evolutionary process to efficiently find a virtual texture model that matched or exceeded the realism of a data-driven 
  model. 
  Furthermore, for 4 out of 5 real textures, 80% of the preference-driven models from participants were rated comparable to the data-driven models.
</p>
<br/>
</div>


<div class="w-container">
        <p class="text_italic">Our results: </p>
<!--    <video width="100%" height="100%" source="" src="https://www.youtube.com/watch?v=8-nCkeeR6o0" type="video/mp4" loop="true" autoplay="autoplay" controls muted></video> -->
<!--    <iframe src="https://drive.google.com/file/d/1QgW2gofJOiJHRRNV7t2A1nDBHfiV9a3e/preview" style=" width: 100%; height: 100%; border: none;"></iframe> -->
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