Anime-to-Real Clothing: Cosplay Costume Generation via Image-to-Image Translation
Koya Tango, Marie Katsurai, Hayato Maki, Ryosuke Goto

TL;DR
This paper introduces a novel GAN-based method for converting anime images into realistic cosplay costumes, addressing diverse styles and shapes with improved quality and realism compared to existing approaches.
Contribution
The paper presents a new GAN architecture and a dataset preprocessing pipeline for high-quality anime-to-real costume image translation.
Findings
Outperforms existing methods in quality metrics
Generates more realistic cosplay images
Effective handling of diverse clothing styles
Abstract
Cosplay has grown from its origins at fan conventions into a billion-dollar global dress phenomenon. To facilitate imagination and reinterpretation from animated images to real garments, this paper presents an automatic costume image generation method based on image-to-image translation. Cosplay items can be significantly diverse in their styles and shapes, and conventional methods cannot be directly applied to the wide variation in clothing images that are the focus of this study. To solve this problem, our method starts by collecting and preprocessing web images to prepare a cleaned, paired dataset of the anime and real domains. Then, we present a novel architecture for generative adversarial networks (GANs) to facilitate high-quality cosplay image generation. Our GAN consists of several effective techniques to fill the gap between the two domains and improve both the global and local…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Human Motion and Animation · 3D Shape Modeling and Analysis
