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

• URL: http://arxiv.org/abs/2605.24894v1
• Date: Sun, 24 May 2026 06:46:52 GMT
• Title: BFS: Back-to-Front Layered Image Synthesis via Knowledge Transfer
• Abstract summary: layered image synthesis is a promising direction for controllable and creative editing.<n>We propose BFS, a generation-based framework for layered image synthesis.<n>We show that BFS produces high-quality layered images, consistently outperforming prior methods.
• Score: 16.6842195195723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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