OSInsert: Towards High-authenticity and High-fidelity Image Composition

2026-02-23

AI Summary

Existing image synthesis methods struggle to simultaneously achieve high realism—ensuring semantic and pose consistency between foreground and background—and high fidelity in preserving foreground details. This work proposes a novel two-stage generative framework that decouples these objectives for the first time: the first stage synthesizes a foreground deformation compatible with the background to guarantee realism, while the second stage refines this result to reconstruct high-fidelity details. Evaluated on the MureCOM dataset, the proposed approach significantly outperforms current single-stage methods, achieving both natural scene integration and faithful detail preservation. The code and models are publicly released.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Web data generation and simulation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Generative image composition aims to regenerate the given foreground object in the background image to produce a realistic composite image. Some high-authenticity methods can adjust foreground pose/view to be compatible with background, while some high-fidelity methods can preserve the foreground details accurately. However, existing methods can hardly achieve both goals at the same time. In this work, we propose a two-stage strategy to achieve both goals. In the first stage, we use high-authenticity method to generate reasonable foreground shape, serving as the condition of high-fidelity method in the second stage. The experiments on MureCOM dataset verify the effectiveness of our two-stage strategy. The code and model have been released at https://github.com/bcmi/OSInsert-Image-Composition.

Problem

• image composition
• high-authenticity
• high-fidelity
• foreground-background integration
• generative image synthesis

Innovation

• two-stage strategy
• high-authenticity
• high-fidelity
• image composition
• generative modeling