title: Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile
    Virtual Clothing Tasks
summary: 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/
paper_authors._id: 679c23c74ca5036d02b91927
paper_authors.name: Hailong Guo
paper_authors._id: 679c23c74ca5036d02b91928
paper_authors.name: Bohan Zeng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-31T08:35:59.690Z
paper_authors._id: 6671214c92412fd4640714eb
paper_authors.avatarUrl: /avatars/48fa84e7bc3bb92ad0192aa26b32de10.svg
paper_authors.fullname: bohan zeng
paper_authors.type: user
paper_authors.user: zbhpku
paper_authors._id: 679c23c74ca5036d02b91929
paper_authors.name: Yiren Song
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-04T09:39:43.380Z
paper_authors._id: 64311a95034ecbefddd141ef
paper_authors.avatarUrl: /avatars/b6dc5ca373bedbaa368208517954c375.svg
paper_authors.fullname: Yiren Song
paper_authors.type: user
paper_authors.user: yiren98
paper_authors._id: 679c23c74ca5036d02b9192a
paper_authors.name: Wentao Zhang
paper_authors._id: 679c23c74ca5036d02b9192b
paper_authors.name: Chuang Zhang
paper_authors._id: 679c23c74ca5036d02b9192c
paper_authors.name: Jiaming Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-31T08:35:56.805Z
paper_authors._id: 637c941588699fba70e29f70
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/637c941588699fba70e29f70/b6G_QZkT-MhE47dx87i0d.png
paper_authors.fullname: LIU JIAMING
paper_authors.type: user
paper_authors.user: jamesliu1217
paper_title: Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile
    Virtual Clothing Tasks
paper_summary: 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/
paper_ai_summary: Any2AnyTryon generates high-quality, flexible, and controllable image-based virtual try-ons using adaptive position embedding and a large-scale synthetic dataset.
paper_ai_keywords: 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