FitDiT: Advancing the Authentic Garment Details for High-fidelity
  Virtual Try-on
Authors: name: Boyuan Jiang, name: Xiaobin Hu, name: Donghao Luo, name: Qingdong He, name: Chengming Xu, name: Jinlong Peng, name: Jiangning Zhang, name: Chengjie Wang, name: Yunsheng Wu, name: Yanwei Fu

Although image-based virtual try-on has made considerable progress, emerging
approaches still encounter challenges in producing high-fidelity and robust
fitting images across diverse scenarios. These methods often struggle with
issues such as texture-aware maintenance and size-aware fitting, which hinder
their overall effectiveness. To address these limitations, we propose a novel
garment perception enhancement technique, termed FitDiT, designed for
high-fidelity virtual try-on using Diffusion Transformers (DiT) allocating more
parameters and attention to high-resolution features. First, to further improve
texture-aware maintenance, we introduce a garment texture extractor that
incorporates garment priors evolution to fine-tune garment feature,
facilitating to better capture rich details such as stripes, patterns, and
text. Additionally, we introduce frequency-domain learning by customizing a
frequency distance loss to enhance high-frequency garment details. To tackle
the size-aware fitting issue, we employ a dilated-relaxed mask strategy that
adapts to the correct length of garments, preventing the generation of garments
that fill the entire mask area during cross-category try-on. Equipped with the
above design, FitDiT surpasses all baselines in both qualitative and
quantitative evaluations. It excels in producing well-fitting garments with
photorealistic and intricate details, while also achieving competitive
inference times of 4.57 seconds for a single 1024x768 image after DiT structure
slimming, outperforming existing methods.