# Generate E-commerce Product Background by Integrating Category Commonality and Personalized Style

arXiv: https://arxiv.org/abs/2312.13309 · HTML: https://mlsanity.com/paper/2312.13309/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 42 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 29 of 29)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [Blended Latent Diffusion](https://mlsanity.com/paper/2206.02779/) (2022)
- [Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting](https://mlsanity.com/paper/2212.06909/) (2022)
- [Inpaint Anything: Segment Anything Meets Image Inpainting](https://mlsanity.com/paper/2304.06790/) (2023)
- [RealFill: Reference-Driven Generation for Authentic Image Completion](https://mlsanity.com/paper/2309.16668/) (2023)
- [DreamEdit: Subject-driven Image Editing](https://mlsanity.com/paper/2306.12624/) (2023)
- [Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement](https://mlsanity.com/paper/2008.07467/) (2020)
- [Automated Creative Optimization for E-Commerce Advertising](https://mlsanity.com/paper/2103.00436/) (2021)
- [Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding](https://mlsanity.com/paper/2205.14970/) (2022)
- [Staging E-Commerce Products for Online Advertising using Retrieval Assisted Image Generation](https://mlsanity.com/paper/2307.15326/) (2023)
- [Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure](https://mlsanity.com/paper/2103.01453/) (2021)
- [Automatic Generation of Product-Image Sequence in E-commerce](https://mlsanity.com/paper/2206.12994/) (2022)
- [A Smart System for Selection of Optimal Product Images in E-Commerce](https://mlsanity.com/paper/1811.07996/) (2018)
- [Reference-based Painterly Inpainting via Diffusion: Crossing the Wild Reference Domain Gap](https://mlsanity.com/paper/2307.10584/) (2023)

## Built on by (most cited first, 16 of 16)

- [PosterMaker: Towards High-Quality Product Poster Generation with Accurate Text Rendering](https://mlsanity.com/paper/2504.06632/) (2025)
- [RelaCtrl: Relevance-Guided Efficient Control for Diffusion Transformers](https://mlsanity.com/paper/2502.14377/) (2025)
- [Towards Reliable Advertising Image Generation Using Human Feedback](https://mlsanity.com/paper/2408.00418/) (2024)
- [CTR-Driven Advertising Image Generation with Multimodal Large Language Models](https://mlsanity.com/paper/2502.06823/) (2025)
- [Uni-Layout: Integrating Human Feedback in Unified Layout Generation and Evaluation](https://mlsanity.com/paper/2508.02374/) (2025)
- [AutoPP: Towards Automated Product Poster Generation and Optimization](https://mlsanity.com/paper/2512.21921/) (2025)
- [T-Stars-Poster: A Framework for Product-Centric Advertising Image Design](https://mlsanity.com/paper/2501.14316/) (2025)
- [One Size, Many Fits: Aligning Diverse Group-Wise Click Preferences in Large-Scale Advertising Image Generation](https://mlsanity.com/paper/2602.02033/) (2026)
- [simpleposter: A simple baseline for product poster generation](https://mlsanity.com/paper/2605.08784/) (2026)
- [Teleportraits: Training-Free People Insertion into Any Scene](https://mlsanity.com/paper/2510.05660/) (2025)
- [Self-Reasoning Agentic Framework for Narrative Product Grid-Collage Generation](https://mlsanity.com/paper/2604.16958/) (2026)
- [Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models](https://mlsanity.com/paper/2605.12138/) (2026)
- [Multi-Object Advertisement Creative Generation](https://mlsanity.com/paper/2603.13745/) (2026)
- [CommerceVibe: Learning to Design E-Commerce Creatives as Executable Visual Code via Dual-Feedback Reinforcement Learning](https://mlsanity.com/paper/2608.27893/) (2026)
- [I Am AdMan: A Pipeline for Automatic Generation of Personalized Advertising Imagery](https://mlsanity.com/paper/2609.12694/) (2026)
- [IMFD: End-to-end Multi-Face Forgery Detection through Instruction-based Large Vision-Language Models](https://mlsanity.com/paper/2609.19693/) (2026)

## Abstract (arXiv)

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 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. BG60k will be available at urlhttps://github.com/Whileherham/BG60k.