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<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3c.org/TR/1999/REC-html401-19991224/loose.dtd">
<html xml:lang="en" xmlns="http://www.w3.org/1999/xhtml" lang="en">
<head>
<title>Food Image Transformation Project Page</title>
<meta http-equiv="Content-Type" content="text/html; charset=windows-1252">
<script src="lib.js" type="text/javascript"></script>
<script src="popup.js" type="text/javascript"></script>
<link rel="stylesheet" type="text/css" href="css/style.css" />
<style type="text/css" media="all">
IMG {
PADDING-RIGHT: 0px;
PADDING-LEFT: 0px;
FLOAT: right;
PADDING-BOTTOM: 0px;
PADDING-TOP: 0px
}
#primarycontent {
MARGIN-LEFT: auto;
;
WIDTH: expression(document.body.clientWidth > 1000? "1000px": "auto");
MARGIN-RIGHT: auto;
TEXT-ALIGN: left;
max-width:
1000px
}
BODY {
TEXT-ALIGN: center
}
</style>
<meta content="MSHTML 6.00.2800.1400" name="GENERATOR">
<script src="b5m.js" id="b5mmain" type="text/javascript"></script>
</head>
<body>
<div id="primarycontent">
<center>
<h1>Magical Rice Bowl: A Real-time Food Category Changer</h1>
</center>
<center>
<h2><a href="https://negi111111.github.io/">Ryosuke Tanno</a>
<a href="">Daichi Horita</a>
<a href="">Wataru Shimoda</a>
<a href="http://acc.cs.uec.ac.jp/yanai/index.html">Keiji Yanai</a></h2>
</center>
<center>
<h2><a href="http://mm.cs.uec.ac.jp/e/">Department of Informatics, The University of Electro-Communication</a></h2>
</center>
<center>
<h2>In ACM MM 2018</h2>
</center>
<p></p>
<h2 align='center'></h2>
<table border="0" align="center" cellspacing="0" cellpadding="20">
<td align="center" valign="middle">
<iframe width="560" height="315" src="https://www.youtube.com/embed/nUboBFyy4Cc" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
</td>
</table>
<p>
<h2>Abstract</h2>
<div style="font-size:14px">
<p>In this paper, we demonstrate "Real-time Food Category
Change" based on a Conditional Cycle GAN (cCycle GAN)
with a large-scale food image data collected from the Twit-
ter Stream. Conditional Cycle GAN is an extension of Cy-
cleGAN, which enables "Food Category Change" among ten
kinds of typical foods served in bowl-type dishes such as beef
rice bowl and ramen noodles. The proposed system enables
us to change the appearance of a given food photo according
to the given category keeping the shape of the given food but
exchanging its textures. For training, we used two hundred
and thirty thousand food images which achieved very natural
food category change among ten kinds of typical Japanese
foods: ramen noodle, curry rice, fried rice, beef rice bowl,
chilled noodle, spaghetti with meat source, white rice, eel
bowl, and fried noodle.</p>
</div>
<a href=""><img style="float: left; padding: 10px; PADDING-RIGHT: 30px;" alt="paper thumbnail" src="images/paper_thumbnail.png" width=170></a>
<br>
<h2>Paper</h2>
<p><a href="./demo_mm18_final.pdf">demo_mm18_final.pdf</a>, 2018. </p>
<h2>Citation</h2>
<p>Ryosuke Tanno, Daichi Horita, Wataru Shimoda and Keiji Yanai. "FoodChangeLens:CNN-based Food Transformation on HoloLens", in Proc. of ACM International Conference Multimedia, 2018.
<a href="FoodImageTransformation.txt">Bibtex</a>
</p>
<br>
<br>
<br>
<br>
<h2 align='center'> Another Application</h2>
<table border="0" cellspacing="0" cellpadding="20">
<td align="center" valign="middle">
<h2>Food Transfer Image Museum on HoloLens</h2>
<p><iframe width="480" height="270" src="https://www.youtube.com/embed/IEyKQ1i-fd4" frameborder="0" allowfullscreen></iframe></p>
<div style="width:480px; text-align:left; font-size:14px">Shu Naritomi made the above.</div>
</td>
<td align="center" valign="middle">
<h2>Food Image Transformation using HoloLens</h2>
<p><iframe width="480" height="270" src="https://www.youtube.com/embed/yBM9VgXj9e0" frameborder="0" allowfullscreen></iframe></p>
<div style="width:480px; text-align:left; font-size:14px">Shu Naritomi and Takumi Ege made the above.</div>
</td>
</tr>
</table>
<h1 align="center"> Computer Vision Art Gallery</h1>
In 2018, for the <a href="https://sites.google.com/view/eccvfashion/home">First Workshop on Computer Vision for Fashion, Art and Design</a>, at the European Conference on Computer Vision (ECCV) in Munich, Germany, a call was put out for artworks dealing with computer vision technologies. We were chosen for <a href="https://computervisionart.com/">Longlist Award</a> on ECCV Art Workshop.
<table border="0" width="1000px" cellpadding="10">
<tr>
<td width="500px" align="center" valign="top">
<h2>Paella</h2>
<img src="ECCV_ART2018/0.png" width="500px">
</div>
</td>
<td align="center" width="500px" valign="top">
<h2>Tom Yum Goong </h2>
<img src="ECCV_ART2018/1.png" width="500px">
</td>
</tr>
<tr>
<td width="500px" align="center" valign="top">
<h2>Pizza</h2>
<img src="ECCV_ART2018/2.png" width="500px">
</div>
</td>
<td align="center" width="500px" valign="top">
<h2>Beef Bowl</h2>
<img src="ECCV_ART2018/3.png" width="500px">
</td>
</tr>
<tr>
<td width="500px" align="center" valign="top">
<h2>Magical Rice Bowl !?</h2>
<img src="ECCV_ART2018/4.png" width="500px">
</div>
</td>
<td align="center" width="500px" valign="top">
<h2>Magical Rice Bowl !?</h2>
<img src="ECCV_ART2018/5.png" width="500px">
</td>
</tr>
<tr>
<td width="500px" align="center" valign="top">
<h2>Ramen</h2>
<img src="ECCV_ART2018/6.png" width="500px">
</div>
</td>
<td align="center" width="500px" valign="top">
<h2>Curry</h2>
<img src="ECCV_ART2018/7.png" width="500px">
</td>
</tr>
</table>
<h1 align="center">Ms. Koizumi Loves Ramen Noodles（Japanese Anime）</h1>
Ms. Koizumi Loves Ramen Noodles (ラーメン大好き小泉さん Rāmen Daisuki Koizumi-san) is a Japanese manga series by Naru Narumi. It began serialization in Takeshobo's Manga Life Storia magazine in September 2013. A live-action drama series adaptation aired from June 2015 to December 2016. A 12-episode anime television series adaptation co-animated by Studio Gokumi and AXsiZ aired in Japan between January 4 and March 22, 2018. (by <a href="https://en.wikipedia.org/wiki/Ms._Koizumi_Loves_Ramen_Noodles">wikipedia</a>)
<table border="0" width="1000px" cellpadding="10">
<tr>
<td width="1000px" align="center" valign="top">
<h2></h2>
<img src="anime.png" width="1000px">
</div>
</td>
</tr>
</table>
<h2>Related Work</h2>
<ul id='relatedwork'>
<li>
S. Jiang and Y. Fu. <a href=""><strong>"Fashion Style Generators"</strong></a>, in Proc. of
the Twenty-Sixth International Joint Conference on Artificial
Intelligence, 2017.
</li>
<li>
J. Johnson, A. Alahi, and L.F. Fei <a href=""><strong>"Perceptual Losses for Real-Time Style Transfer and Super-Resolution"</strong></a>, in Proc. of European Conference on Computer Vision, 2016.
</li>
<li>
Y. Matsuda, H. Hoashi, and K. Yanai <a href=""><strong>"Recognition of Multiple-Food Images by Detecting Candidate Regions"</strong></a>, in Proc. of IEEE International Conference on Multimedia and Expo, 2012.
</li>
<li>
A. Odena, C. Olah, and J. Shlens <a href=""><strong>"Conditional Image Synthesis
With Auxiliary Classifier GANs"</strong></a>, in Proc. of IEEE International Conference on Multimedia and Expo, 2012.
</li>
<li>
K. Yanai and Y. Kawano <a href=""><strong>"Twitter Food Image Mining and
Analysis for One Hundred Kinds of Foods"</strong></a>, in Proc. of Pacifit-Rim
Conference on Multimedia (PCM), 2014.
</li>
<li>
K. Yanai and R. Tanno <a href=""><strong>"Conditional Fast Style Transfer Networks"</strong></a>, in Proc. of ACM International Conference on Multimedia Retrieval, 2017.
</li>
<li>
J. Y. Zhu, T. Park, P. Isola, and A. A. Efros <a href=""><strong>"Unpaired
Image-to-Image Translation using Cycle-Consistent Adversarial
Networks"</strong></a>, in Proc. of IEEE International Conference on Computer Vision, 2017.
</li>
</ul>
<br>
<h2>Acknowledgement</h2>
<p>This work was supported by JSPS KAKENHI Grant Number 15H05915, 17H01745, 17H05972, 17H06026 and 17H06100.</p>
</body>
</html>