We present a framework for high-fidelity product image recontextualization using text-to-image diffusion models and a novel data augmentation pipeline. This pipeline leverages image-to-video diffusion, in/outpainting & negatives to create synthetic training data, addressing limitations of real-world data collection for this task. Our method improves the quality and diversity of generated images by disentangling product representations and enhancing the model's understanding of product characteristics. Evaluation on the ABO dataset and a private product dataset, using automated metrics and human assessment, demonstrates the effectiveness of our framework in generating realistic and compelling product visualizations, with implications for applications such as e-commerce and virtual product showcasing.

本文研究了如何利用文本到图像的扩散模型及新颖的数据增强流程，实现高保真的产品图像重新背景化，解决了现实数据收集的局限性。研究结果表明，该框架显著提高了生成图像的质量和多样性，特别是在电子商务和虚拟产品展示等应用中具有重要影响。

利用扩散模型在大规模图像重新背景化中保持产品保真度