Generate E-commerce Product Background by Integrating Category Commonality and Personalized Style
Haohan Wang, Wei Feng, Yaoyu Li, Zheng Zhang, Jingjing Lv, Junjie, Shen, Zhangang Lin, Jingping Shao

TL;DR
This paper introduces a novel diffusion model-based approach for large-scale e-commerce product background generation, integrating category commonality and personalized styles to improve efficiency and customization.
Contribution
It proposes a Category-Wise Generator for scalable background creation and a Personality-Wise Generator for fine-grained personalized styles, along with a new large dataset BG60k.
Findings
High-quality background generation across categories
Effective personalization of styles from reference images
Scalable model for large-scale e-commerce applications
Abstract
The state-of-the-art methods for e-commerce product background generation suffer from the inefficiency of designing product-wise prompts when scaling up the production, as well as the ineffectiveness of describing fine-grained styles when customizing personalized backgrounds for some specific brands. To address these obstacles, we integrate the category commonality and personalized style into diffusion models. Concretely, we propose a Category-Wise Generator to enable large-scale background generation with only one model for the first time. A unique identifier in the prompt is assigned to each category, whose attention is located on the background by a mask-guided cross attention layer to learn the category-wise style. Furthermore, for products with specific and fine-grained requirements in layout, elements, etc, a Personality-Wise Generator is devised to learn such personalized style…
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Video Analysis and Summarization · Generative Adversarial Networks and Image Synthesis
MethodsDiffusion
