title: DreamID: High-Fidelity and Fast diffusion-based Face Swapping via
    Triplet ID Group Learning
summary: In this paper, we introduce DreamID, a diffusion-based face swapping model
  that achieves high levels of ID similarity, attribute preservation, image
  fidelity, and fast inference speed. Unlike the typical face swapping training
  process, which often relies on implicit supervision and struggles to achieve
  satisfactory results. DreamID establishes explicit supervision for face
  swapping by constructing Triplet ID Group data, significantly enhancing
  identity similarity and attribute preservation. The iterative nature of
  diffusion models poses challenges for utilizing efficient image-space loss
  functions, as performing time-consuming multi-step sampling to obtain the
  generated image during training is impractical. To address this issue, we
  leverage the accelerated diffusion model SD Turbo, reducing the inference steps
  to a single iteration, enabling efficient pixel-level end-to-end training with
  explicit Triplet ID Group supervision. Additionally, we propose an improved
  diffusion-based model architecture comprising SwapNet, FaceNet, and ID Adapter.
  This robust architecture fully unlocks the power of the Triplet ID Group
  explicit supervision. Finally, to further extend our method, we explicitly
  modify the Triplet ID Group data during training to fine-tune and preserve
  specific attributes, such as glasses and face shape. Extensive experiments
  demonstrate that DreamID outperforms state-of-the-art methods in terms of
  identity similarity, pose and expression preservation, and image fidelity.
  Overall, DreamID achieves high-quality face swapping results at 512*512
  resolution in just 0.6 seconds and performs exceptionally well in challenging
  scenarios such as complex lighting, large angles, and occlusions.
paper_authors._id: 6809dd092e04f68a3f5baa66
paper_authors.name: Fulong Ye
paper_authors.status: admin_assigned
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paper_authors.fullname: fulong ye
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paper_authors._id: 6809dd092e04f68a3f5baa67
paper_authors.name: Miao Hua
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paper_authors.fullname: miaohua
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paper_authors.user: miaohua
paper_authors._id: 6809dd092e04f68a3f5baa68
paper_authors.name: Pengze Zhang
paper_authors._id: 6809dd092e04f68a3f5baa69
paper_authors.name: Xinghui Li
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paper_authors.fullname: Xinghui Li
paper_authors.type: user
paper_authors.user: Crayon-Shinchan
paper_authors._id: 6809dd092e04f68a3f5baa6a
paper_authors.name: Qichao Sun
paper_authors.status: admin_assigned
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paper_authors.fullname: Qichao Sun
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paper_authors.user: giruhc9gj
paper_authors._id: 6809dd092e04f68a3f5baa6b
paper_authors.name: Songtao Zhao
paper_authors._id: 6809dd092e04f68a3f5baa6c
paper_authors.name: Qian He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-24T11:22:32.939Z
paper_authors._id: 645dcad7a19f3e64bbf35e6c
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paper_authors.fullname: Qian He
paper_authors.type: user
paper_authors.user: heqian
paper_authors._id: 6809dd092e04f68a3f5baa6d
paper_authors.name: Xinglong Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-24T11:22:21.048Z
paper_authors._id: 67bc6b515d9470ec64bdcc33
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/dttL8Fb3bKCVG5zjg_02q.png
paper_authors.fullname: Xinglong Wu
paper_authors.type: user
paper_authors.user: Xingzhe-xlwu
paper_title: DreamID: High-Fidelity and Fast diffusion-based Face Swapping via
    Triplet ID Group Learning
paper_summary: In this paper, we introduce DreamID, a diffusion-based face swapping model
  that achieves high levels of ID similarity, attribute preservation, image
  fidelity, and fast inference speed. Unlike the typical face swapping training
  process, which often relies on implicit supervision and struggles to achieve
  satisfactory results. DreamID establishes explicit supervision for face
  swapping by constructing Triplet ID Group data, significantly enhancing
  identity similarity and attribute preservation. The iterative nature of
  diffusion models poses challenges for utilizing efficient image-space loss
  functions, as performing time-consuming multi-step sampling to obtain the
  generated image during training is impractical. To address this issue, we
  leverage the accelerated diffusion model SD Turbo, reducing the inference steps
  to a single iteration, enabling efficient pixel-level end-to-end training with
  explicit Triplet ID Group supervision. Additionally, we propose an improved
  diffusion-based model architecture comprising SwapNet, FaceNet, and ID Adapter.
  This robust architecture fully unlocks the power of the Triplet ID Group
  explicit supervision. Finally, to further extend our method, we explicitly
  modify the Triplet ID Group data during training to fine-tune and preserve
  specific attributes, such as glasses and face shape. Extensive experiments
  demonstrate that DreamID outperforms state-of-the-art methods in terms of
  identity similarity, pose and expression preservation, and image fidelity.
  Overall, DreamID achieves high-quality face swapping results at 512*512
  resolution in just 0.6 seconds and performs exceptionally well in challenging
  scenarios such as complex lighting, large angles, and occlusions.
paper_ai_summary: A diffusion-based face swapping model, DreamID, achieves high identity similarity, attribute preservation, and fast inference by using explicit Triplet ID Group supervision and an improved diffusion architecture.
paper_ai_keywords: diffusion-based face swapping model Triplet ID Group data diffusion models explicit supervision SD Turbo SwapNet FaceNet ID Adapter identity similarity attribute preservation image fidelity pose and expression preservation complex lighting large angles occlusions