title: DiffDecompose: Layer-Wise Decomposition of Alpha-Composited Images via
    Diffusion Transformers
summary: Diffusion models have recently motivated great success in many generation
  tasks like object removal. Nevertheless, existing image decomposition methods
  struggle to disentangle semi-transparent or transparent layer occlusions due to
  mask prior dependencies, static object assumptions, and the lack of datasets.
  In this paper, we delve into a novel task: Layer-Wise Decomposition of
  Alpha-Composited Images, aiming to recover constituent layers from single
  overlapped images under the condition of semi-transparent/transparent alpha
  layer non-linear occlusion. To address challenges in layer ambiguity,
  generalization, and data scarcity, we first introduce AlphaBlend, the first
  large-scale and high-quality dataset for transparent and semi-transparent layer
  decomposition, supporting six real-world subtasks (e.g., translucent flare
  removal, semi-transparent cell decomposition, glassware decomposition).
  Building on this dataset, we present DiffDecompose, a diffusion
  Transformer-based framework that learns the posterior over possible layer
  decompositions conditioned on the input image, semantic prompts, and blending
  type. Rather than regressing alpha mattes directly, DiffDecompose performs
  In-Context Decomposition, enabling the model to predict one or multiple layers
  without per-layer supervision, and introduces Layer Position Encoding Cloning
  to maintain pixel-level correspondence across layers. Extensive experiments on
  the proposed AlphaBlend dataset and public LOGO dataset verify the
  effectiveness of DiffDecompose. The code and dataset will be available upon
  paper acceptance. Our code will be available at:
  https://github.com/Wangzt1121/DiffDecompose.
paper_authors._id: 6840f79ceb249b555b244efc
paper_authors.name: Zitong Wang
paper_authors._id: 6840f79ceb249b555b244efd
paper_authors.name: Hang Zhao
paper_authors._id: 6840f79ceb249b555b244efe
paper_authors.name: Qianyu Zhou
paper_authors._id: 6840f79ceb249b555b244eff
paper_authors.name: Xuequan Lu
paper_authors._id: 6840f79ceb249b555b244f00
paper_authors.name: Xiangtai Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T12:57:04.933Z
paper_authors._id: 63958b4414513eaf9029ebf1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/U1g5H071pWRswGAG9UTpo.png
paper_authors.fullname: Xiangtai Li
paper_authors.type: user
paper_authors.user: LXT
paper_authors._id: 6840f79ceb249b555b244f01
paper_authors.name: Yiren Song
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T12:56:53.866Z
paper_authors._id: 64311a95034ecbefddd141ef
paper_authors.avatarUrl: /avatars/b6dc5ca373bedbaa368208517954c375.svg
paper_authors.fullname: Yiren Song
paper_authors.type: user
paper_authors.user: yiren98
paper_title: DiffDecompose: Layer-Wise Decomposition of Alpha-Composited Images via
    Diffusion Transformers
paper_summary: Diffusion models have recently motivated great success in many generation
  tasks like object removal. Nevertheless, existing image decomposition methods
  struggle to disentangle semi-transparent or transparent layer occlusions due to
  mask prior dependencies, static object assumptions, and the lack of datasets.
  In this paper, we delve into a novel task: Layer-Wise Decomposition of
  Alpha-Composited Images, aiming to recover constituent layers from single
  overlapped images under the condition of semi-transparent/transparent alpha
  layer non-linear occlusion. To address challenges in layer ambiguity,
  generalization, and data scarcity, we first introduce AlphaBlend, the first
  large-scale and high-quality dataset for transparent and semi-transparent layer
  decomposition, supporting six real-world subtasks (e.g., translucent flare
  removal, semi-transparent cell decomposition, glassware decomposition).
  Building on this dataset, we present DiffDecompose, a diffusion
  Transformer-based framework that learns the posterior over possible layer
  decompositions conditioned on the input image, semantic prompts, and blending
  type. Rather than regressing alpha mattes directly, DiffDecompose performs
  In-Context Decomposition, enabling the model to predict one or multiple layers
  without per-layer supervision, and introduces Layer Position Encoding Cloning
  to maintain pixel-level correspondence across layers. Extensive experiments on
  the proposed AlphaBlend dataset and public LOGO dataset verify the
  effectiveness of DiffDecompose. The code and dataset will be available upon
  paper acceptance. Our code will be available at:
  https://github.com/Wangzt1121/DiffDecompose.
paper_ai_summary: DiffDecompose, a diffusion Transformer-based framework, effectively decomposes images into constituent layers with semantic prompts, addressing challenges in transparent layer decomposition.
paper_ai_keywords: diffusion models diffusion Transformer posterior semantic prompts blending type In-Context Decomposition Layer Position Encoding Cloning AlphaBlend dataset translucent flare removal semi-transparent cell decomposition glassware decomposition LOGO dataset