[2204.12151] ClothFormer:Taming Video Virtual Try-on in All Module

The task of video virtual try-on aims to fit the target clothes to a person in the video with spatio-temporal consistency. Despite tremendous progress of image virtual try-on, they lead to inconsistency between frames when applied to videos. Limited work also explored the task of video-based virtual try-on but failed to produce visually pleasing and temporally coherent results. Moreover, there are two other key challenges: 1) how to generate accurate warping when occlusions appear in the clothing region; 2) how to generate clothes and non-target body parts (e.g. arms, neck) in harmony with the complicated background; To address them, we propose a novel video virtual try-on framework, ClothFormer, which successfully synthesizes realistic, harmonious, and spatio-temporal consistent results in complicated environment. In particular, ClothFormer involves three major modules. First, a two-stage anti-occlusion warping module that predicts an accurate dense flow mapping between the body regio

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@Maxwell_110
@Maxwell_110 ClothFormer 📝 t.co/FWimNtYcVk ClothFormer は Video Virtual Try-on のモデル Warp/Tracking/Try-On module から成っており, 服を着た人の画像(着衣画像)の服をマスクし,マスクした服の「カタログ画像」を人物に try-on させ,着衣画像を再生するような自己教師有学習を行う 1/ t.co/pq8yTCD3Lt

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