DiffDecompose: Layer-Wise Decomposition of Alpha-Composited Images via
  Diffusion Transformers
Authors: name: Zitong Wang, name: Hang Zhao, name: Qianyu Zhou, name: Xuequan Lu, name: Xiangtai Li, name: Yiren Song

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.