AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenario

• URL: http://arxiv.org/abs/2405.18172v1
• Date: Tue, 28 May 2024 13:33:08 GMT
• Title: AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenario
• Authors: Yuhan Li, Hao Zhou, Wenxiang Shang, Ran Lin, Xuanhong Chen, Bingbing Ni,
• Abstract summary: AnyFit surpasses all baselines on high-resolution benchmarks and real-world data by a large gap. AnyFit's impressive performance on high-fidelity virtual try-ons in any scenario from any image, paves a new path for future research within the fashion community.
• Score: 50.62711489896909
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: While image-based virtual try-on has made significant strides, emerging approaches still fall short of delivering high-fidelity and robust fitting images across various scenarios, as their models suffer from issues of ill-fitted garment styles and quality degrading during the training process, not to mention the lack of support for various combinations of attire. Therefore, we first propose a lightweight, scalable, operator known as Hydra Block for attire combinations. This is achieved through a parallel attention mechanism that facilitates the feature injection of multiple garments from conditionally encoded branches into the main network. Secondly, to significantly enhance the model's robustness and expressiveness in real-world scenarios, we evolve its potential across diverse settings by synthesizing the residuals of multiple models, as well as implementing a mask region boost strategy to overcome the instability caused by information leakage in existing models. Equipped with the above design, AnyFit surpasses all baselines on high-resolution benchmarks and real-world data by a large gap, excelling in producing well-fitting garments replete with photorealistic and rich details. Furthermore, AnyFit's impressive performance on high-fidelity virtual try-ons in any scenario from any image, paves a new path for future research within the fashion community.

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