@techreport{oai:ipsj.ixsq.nii.ac.jp:00217849,
 author = {虫明, 大貴 and 浮田, 宗伯 and Daiki, Mushiake and Norimichi, Ukita},
 issue = {36},
 month = {May},
 note = {視線推定は様々なタスクに応用されており，近年では深層学習を用いた手法が数多く提案されているが，アノテーションコストが膨大である．この為，自動アノテーション済みの CG 画像をスタイル変換することで学習用の画像を生成する手法が提案されている．しかし，学習用の画像への変換前後で虹彩や瞼などが画素単位でずれてしまい，視線推定器の学習に悪影響を与えてしまうという問題がある．本研究ではこの変換前後の画像において画素単位での密な対応付けを行い，対応付けられた画素同士は同じ画像座標上にある，という制約をスタイル変換を行う生成器に与えることで，虹彩や瞼のずれを抑制し，視線推定精度を向上させることを目的とする．, Gaze estimation has been applied to a variety of tasks, and many methods using deep learning have been proposed in recent years, but the annotation cost for training data is enormous. For this reason, a method has been proposed to generate training images by transforming automatically annotated CG images into real images using a style transfer. However, there is a problem that the iris and eyelids are shifted by pixel before and after the transformation to the training image, which adversely affects the training of the gaze estimator. The purpose of this study is to improve the accuracy of gaze estimator by suppressing the displacement of the iris and eyelids through a dense correspondence by pixels between the images before and after the transformation and by constraining the style transformer to keep the corresponding pixels on the same image coordinates.},
 title = {スタイル変換前後の密な画素対応付けによる形状不変スタイル変換とその視線推定への応用},
 year = {2022}
}