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[DL輪読会] The Conditional Analogy GAN: Swapping Fashion Articles on People Images

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Deep Learning JP
Deep Learning JP

2017/9/25 Deep Learning JP: http://deeplearning.jp/seminar-2/

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DEEP LEARNING JP [DL Papers] “The Conditional Analogy GAN: Swapping Fashion Articles on People Images” Ryosuke Goto, VASILY, Inc. http://deeplearning.jp/
2 • The Conditional Analogy GAN: Swapping Fashion Articles on People Images • 著者 • Nikolay Jetchev, Urs Bergmann • Zalando Research • 選定理由 • 服の着せ替えの仕組みをサービスとして実用化したい • 問題設定がシンプルでよい 書誌情報
3 • ドイツのファッションECサイト • ヨーロッパ各国に展開 • 売上はzozotownの7倍 • 技術ブログ • https://jobs.zalando.com/tech/blog/ Zalando?
4 • 入力xiとyiとyjの関係を学習し、xiとyjに対応するxi jを生成するCA-GANを提案 • 服を着ているモデルに別の服を着せ替えることができる Abstract
• 利用するデータの種類 • 置き撮り画像(Article) • モデル着用画像(Human) • ECサイトで入手しやすいペア • どんな商品でも用意されている • 集めるのが簡単 5 Introduction Article Human
6 • ファッションビジネスにおける課題 • Humanは着用イメージが湧きやすいため、購買にとって重要 • しかし、Humanの作成は高価で時間がかかる • 置き撮りから生成できると有り難い Introduction
7 • バーチャル試着への応用 • 服の3Dモデル作成の必要 • 写真を取るよりコスト高 • Articleから生成したい Introduction https://www.slideshare.net/metatechnology/ magic-mirror-for-fashion-stores
8 • CAGAN • xiとyi Proposed Model Article Human
9 • Generator • 入力 • トリプレット • 出力 • 着せ替えイメージ • フィルタ • Discriminator • 入力 • Human, Articleのペア Networks Real/Fake
10 • 3つの損失関数を重み付けしたものを学習 • cGAN（通常のGAN アーキテクチャ） • id loss （フィルターの学習に掛かる制約） • cycle loss （生成物を元に戻した際の差分） Loss Function
11 • 各組み合わせをReal/Fakeで識別 • 着せ替え後の生成物はFake cGAN
12 • Generatorのアウトプット • フィルターと重ねる前のイメージ • できるだけフィルター範囲を小さく取りたい id loss
13 • 着せ替え後のモデルに元の服を着せる • 生成結果が整合性を保つために必要 Cycle Loss
14 • ADAM (lr = 0.0002) • minibatch: 16 • 3つのlossの比: 1.0 : 0.1 : 1.0 • input 128×98 pixel image Experiments
15 • 首周りもきちんと着せ替えできている • 着せ替え部分のみに反応するフィルターを獲得 Results
16 • 特定のHumanに様々なArticleを着せ替え • 横縞のようなtextureの着せ替えが苦手 Results
17 • 様々なHumanに特定のArticleを着せ替え • 顔が崩れる… Results
18 • 手に入れやすいHumanとArticleのデータで、精度の高い着せ替えに成功 • 色の着せ替えはうまくいく一方で、横縞などのtexsureの着せ替えが苦手 • future work • 今回は、背景が単純な画像のみのデータセット • 外で撮ったスナップ写真などはより難しいタスクになる まとめ

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