AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenario
<|reference_start|>AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenario: 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.<|reference_end|>
@article{li2024anyfit:,
    title={AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across
  Any Scenario},
    author={Yuhan Li, Hao Zhou, Wenxiang Shang, Ran Lin, Xuanhong Chen, Bingbing
  Ni},
    journal={arXiv preprint arXiv:2405.18172},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18172},
    primaryClass={cs.CV cs.AI cs.LG}
}