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 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 directly from a reference image to resolve
textual ambiguities, and is trained in a self-supervised manner for more
efficient training data usage. To advance research in this field, the first
large-scale e-commerce product background generation dataset BG60k is
constructed, which covers more than 60k product images from over 2k categories.
Experiments demonstrate that our method could generate high-quality backgrounds
for different categories, and maintain the personalized background style of
reference images. The link to BG60k and codes will be available soon.

针对电子商务产品背景生成的当前方法在规模化生产时设计产品提示的效率低下，以及在定制某些特定品牌的个性化背景时描述细粒度风格的效果不佳的问题，本研究将分类共同性和个性化风格整合到扩散模型中。实验证明我们的方法能够为不同种类生成高质量背景，并保持参考图像的个性化背景风格。