RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition

• URL: http://arxiv.org/abs/2605.11818v1
• Date: Tue, 12 May 2026 09:09:01 GMT
• Title: RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition
• Abstract summary: We propose RevealLayer, a diffusion-based framework that decomposes an RGB image into multiple RGBA layers.<n>To support training and evaluation, we introduce RevealLayer-100K, a high-quality multi-layer natural image constructed through a collaboration between automated algorithms and human annotation.
• Score: 16.33087128655428
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent diffusion-based approaches have made substantial progress in image layer decomposition. However, accurately decomposing complex natural images remains challenging due to difficulties in occlusion completion, robust layer disentanglement, and precise foreground boundaries. Moreover, the scarcity of high-quality multi-layer natural image datasets limits advancement. To address these challenges, we propose RevealLayer, a diffusion-based framework that decomposes an RGB image into multiple RGBA layers, enabling precise layer separation and reliable recovery of occluded content in natural images. RevealLayer incorporates three key components: (1) a Region-Aware Attention module to disentangle hidden and visible layers; (2) an Occlusion-Guided Adapter to leverage contextual information to enhance overlapping regions; and (3) a composite loss to enforce sharp alpha boundaries and suppress residual artifacts. To support training and evaluation, we introduce RevealLayer-100K, a high-quality multi-layer natural image constructed through a collaboration between automated algorithms and human annotation, and further establish RevealLayerBench for benchmarking layer decomposition in general natural scenes. Extensive experiments demonstrate that RevealLayer consistently outperforms existing approaches in layer decomposition.

Related papers

• LimeCross: Context-Conditioned Layered Image Editing with Structural Consistency [7.077690782604656]
  We propose LimeCross, a training-free context-conditioned layered image editing framework.<n>It edits user-selected RGBA layers according to text while keeping the remaining layers unchanged.<n>Experiments demonstrate that LimeCross improves layer purity and composite realism over strong editing baselines.
  arXiv  Detail & Related papers  (2026-05-11T10:18:28Z)
• Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition [73.43121650616804]
  We propose textbfQwen-Image-Layered, an end-to-end diffusion model that decomposes a single RGB image into multiple semantically disentangled RGBA layers.<n>Our method significantly surpasses existing approaches in decomposition quality and establishes a new paradigm for consistent image editing.
  arXiv  Detail & Related papers  (2025-12-17T17:12:42Z)
• DiffDecompose: Layer-Wise Decomposition of Alpha-Composited Images via Diffusion Transformers [85.1185656296496]
  We present DiffDecompose, a diffusion Transformer-based framework that learns the posterior over possible layer decompositions conditioned on the input image.<n>The code and dataset will be available upon paper acceptance.
  arXiv  Detail & Related papers  (2025-05-24T16:08:04Z)
• PSDiffusion: Harmonized Multi-Layer Image Generation via Layout and Appearance Alignment [23.67447416568964]
  Transparent image layer generation plays a significant role in digital art and design.<n>Existing methods typically decompose transparent layers from a single RGB image using a set of tools or generate multiple transparent layers sequentially.<n>We propose PSDiffusion, a unified diffusion framework that leverages image composition priors from pre-trained image diffusion model for simultaneous multi-layer text-to-image generation.
  arXiv  Detail & Related papers  (2025-05-16T17:23:35Z)
• DreamLayer: Simultaneous Multi-Layer Generation via Diffusion Mode [47.32061459437175]
  We introduce DreamLayer, a framework that enables coherent text-driven generation of multiple image layers.<n>By explicitly modeling the relationship between transparent foreground and background layers, DreamLayer builds inter-layer connections.<n>Experiments and user studies demonstrate that DreamLayer generates more coherent and well-aligned layers.
  arXiv  Detail & Related papers  (2025-03-17T05:34:11Z)
• Generative Image Layer Decomposition with Visual Effects [49.75021036203426]
  LayerDecomp is a generative framework for image layer decomposition.<n>It produces clean backgrounds and high-quality transparent foregrounds with faithfully preserved visual effects.<n>Our method achieves superior quality in layer decomposition, outperforming existing approaches in object removal and spatial editing tasks.
  arXiv  Detail & Related papers  (2024-11-26T20:26:49Z)
• MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generation [54.64194935409982]
  We introduce MuLAn: a novel dataset comprising over 44K MUlti-Layer-wise RGBA decompositions. MuLAn is the first photorealistic resource providing instance decomposition and spatial information for high quality images. We aim to encourage the development of novel generation and editing technology, in particular layer-wise solutions.
  arXiv  Detail & Related papers  (2024-04-03T14:58:00Z)
• LayerDiff: Exploring Text-guided Multi-layered Composable Image Synthesis via Layer-Collaborative Diffusion Model [70.14953942532621]
  Layer-collaborative diffusion model, named LayerDiff, is designed for text-guided, multi-layered, composable image synthesis. Our model can generate high-quality multi-layered images with performance comparable to conventional whole-image generation methods. LayerDiff enables a broader range of controllable generative applications, including layer-specific image editing and style transfer.
  arXiv  Detail & Related papers  (2024-03-18T16:28:28Z)
• Neural Spline Fields for Burst Image Fusion and Layer Separation [40.9442467471977]
  We propose a versatile intermediate representation: a two-layer alpha-composited image plus flow model constructed with neural spline fields. Our method is able to jointly fuse a burst image capture into one high-resolution reconstruction and decompose it into transmission and obstruction layers. We find that, with no post-processing steps or learned priors, our generalizable model is able to outperform existing dedicated single-image and multi-view obstruction removal approaches.
  arXiv  Detail & Related papers  (2023-12-21T18:54:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.