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

2026-05-12

AI Summary

This work addresses the challenges of layer decomposition in natural images—namely, difficulty in occlusion inpainting, non-robust disentanglement, and ambiguous boundaries—exacerbated by the absence of high-quality multi-layer datasets. The authors propose a diffusion-based layer decomposition framework that leverages region-aware attention, an occlusion-guided adapter, and a composite loss function to achieve precise RGBA layer separation and faithful reconstruction of occluded content. A key innovation is the introduction of an occlusion-aware mechanism that implicitly disentangles visible and hidden layers. To support this research, they also construct RevealLayer-100K, the first human-in-the-loop annotated multi-layer dataset, along with RevealLayerBench, a comprehensive benchmark for evaluation. Experiments demonstrate that the proposed method significantly outperforms existing approaches in layer accuracy, alpha matte sharpness, and reliability of occlusion recovery.

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.

Problem

• image layer decomposition
• occlusion completion
• layer disentanglement
• foreground boundaries
• multi-layer natural images

Innovation

• layer decomposition
• occlusion-aware
• diffusion model
• Region-Aware Attention
• multi-layer image dataset