Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and ClothingDownload PDF

Published: 01 Feb 2023, Last Modified: 23 Feb 2023ICLR 2023 posterReaders: Everyone
Keywords: Deep Learning, Virtual Try-on, Generative Adversarial Networks, Artificial Intelligence in Fashion
TL;DR: We develop a special 2D virtual try-on network for cross-category try on task, e.g. long sleeves<->short sleeves or long pants<->skirts, since the limb may be exposed or hidden in such case.
Abstract: Deep learning based virtual try-on system has achieved some encouraging progress recently, but there still remain several big challenges that need to be solved, such as trying on arbitrary clothes of all types, trying on the clothes from one category to another and generating image-realistic results with few artifacts. To handle this issue, we propose the Arbitrary Virtual Try-On Network (AVTON) that is utilized for all-type clothes, which can synthesize realistic try-on images by preserving and trading off characteristics of the target clothes and the reference person. Our approach includes three modules: 1) Limbs Prediction Module, which is utilized for predicting the human body parts by preserving the characteristics of the reference person. This is especially good for handling cross-category try-on task (e.g., long sleeves \(\leftrightarrow\) short sleeves or long pants \(\leftrightarrow\) skirts, etc.), where the exposed arms or legs with the skin colors and details can be reasonably predicted; 2) Improved Geometric Matching Module, which is designed to warp clothes according to the geometry of the target person. We improve the TPS-based warping method with a compactly supported radial function (Wendland's \(\Psi\)-function); 3) Trade-Off Fusion Module, which is to trade off the characteristics of the warped clothes and the reference person. This module is to make the generated try-on images look more natural and realistic based on a fine-tuning symmetry of the network structure. Extensive simulations are conducted and our approach can achieve better performance compared with the state-of-the-art virtual try-on methods.
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