Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile
  Virtual Clothing Tasks
Authors: name: Hailong Guo, name: Bohan Zeng, name: Yiren Song, name: Wentao Zhang, name: Chuang Zhang, name: Jiaming Liu

Image-based virtual try-on (VTON) aims to generate a virtual try-on result by
transferring an input garment onto a target person's image. However, the
scarcity of paired garment-model data makes it challenging for existing methods
to achieve high generalization and quality in VTON. Also, it limits the ability
to generate mask-free try-ons. To tackle the data scarcity problem, approaches
such as Stable Garment and MMTryon use a synthetic data strategy, effectively
increasing the amount of paired data on the model side. However, existing
methods are typically limited to performing specific try-on tasks and lack
user-friendliness. To enhance the generalization and controllability of VTON
generation, we propose Any2AnyTryon, which can generate try-on results based on
different textual instructions and model garment images to meet various needs,
eliminating the reliance on masks, poses, or other conditions. Specifically, we
first construct the virtual try-on dataset LAION-Garment, the largest known
open-source garment try-on dataset. Then, we introduce adaptive position
embedding, which enables the model to generate satisfactory outfitted model
images or garment images based on input images of different sizes and
categories, significantly enhancing the generalization and controllability of
VTON generation. In our experiments, we demonstrate the effectiveness of our
Any2AnyTryon and compare it with existing methods. The results show that
Any2AnyTryon enables flexible, controllable, and high-quality image-based
virtual try-on generation.https://logn-2024.github.io/Any2anyTryonProjectPage/