BFS: Back-to-Front Layered Image Synthesis via Knowledge Transfer

2026-05-24

AI Summary

Existing layered image synthesis methods face limitations in foreground-background separation, data availability, synthesis quality, and scene diversity. This work proposes the BFS framework, which, for the first time, transfers knowledge from non-layered image synthesis to layered generation. BFS employs a dual-branch diffusion model that jointly synthesizes a foreground layer—complete with visual effects such as shadows and reflections—and a composite image, ensuring photorealism and coherence. To address data scarcity, the method introduces a two-stage training strategy that requires only high-quality non-layered images. Experimental results and user studies demonstrate that BFS significantly outperforms current approaches in terms of synthesis quality, visual consistency, and scene diversity.

Abstract

As generative models expand the possibilities of visual content creation, layered image synthesis has emerged as a promising direction for controllable and creative editing. However, existing methods struggle to fully realize this potential. Decomposition-based methods often struggle with clean separation, while generation-based methods suffer from difficulty in training data acquisition, reducing quality and scene diversity. In this paper, we propose BFS, a novel generation-based framework for layered image synthesis. Specifically, given a background image and user guidance, BFS synthesizes a foreground layer that incorporates not only a foreground object but also its associated visual effects, such as shadows and reflections, while seamlessly harmonizing with the background to produce a coherent composite. To enable diverse and high-quality foreground layer synthesis while overcoming data scarcity, we leverage the comparatively easy-to-learn knowledge of unlayered image synthesis for the foreground synthesis. To this end, we adopt a dual-branch diffusion framework in which two interconnected branches generate a composite image and a foreground layer, respectively, enabling bidirectional knowledge transfer. Based on this framework, we propose a two-stage training scheme that utilizes a high-quality unlayered composite image dataset to effectively enhance foreground quality. Extensive experiments, including a user study, show that BFS produces high-quality layered images, consistently outperforming prior methods.

Problem

• layered image synthesis
• foreground layer generation
• data scarcity
• image harmonization
• visual effects

Innovation

• layered image synthesis
• knowledge transfer
• dual-branch diffusion
• foreground harmonization
• two-stage training