Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile
  Virtual Clothing Tasks
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/
679c23c74ca5036d02b91927
Hailong Guo
679c23c74ca5036d02b91928
Bohan Zeng
claimed_verified
2025-01-31T08:35:59.690Z
6671214c92412fd4640714eb
/avatars/48fa84e7bc3bb92ad0192aa26b32de10.svg
bohan zeng
user
zbhpku
679c23c74ca5036d02b91929
Yiren Song
claimed_verified
2025-02-04T09:39:43.380Z
64311a95034ecbefddd141ef
/avatars/b6dc5ca373bedbaa368208517954c375.svg
Yiren Song
user
yiren98
679c23c74ca5036d02b9192a
Wentao Zhang
679c23c74ca5036d02b9192b
Chuang Zhang
679c23c74ca5036d02b9192c
Jiaming Liu
claimed_verified
2025-01-31T08:35:56.805Z
637c941588699fba70e29f70
https://cdn-avatars.huggingface.co/v1/production/uploads/637c941588699fba70e29f70/b6G_QZkT-MhE47dx87i0d.png
LIU JIAMING
user
jamesliu1217
Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile
  Virtual Clothing Tasks
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/
Any2AnyTryon generates high-quality, flexible, and controllable image-based virtual try-ons using adaptive position embedding and a large-scale synthetic dataset.
Image-based virtual try-on VTON garment-model data synthetic data strategy mask-free try-ons adaptive position embedding virtual try-on dataset LAION-Garment generalization controllability