CommerceGen-Studio: An End-to-End Framework for Commercial-Grade Main-Image Generation

A team of researchers posted CommerceGen-Studio to arXiv on 14 Oct 2026, an end-to-end framework for generating commercial-grade main images for e-commerce SKUs that runs in production at millions of main images per day with a latency of a few seconds per image. The framework combines a Prompt-Eval-Refine-Loop Agent (PERL-Agent), Layout Thinker Optimization (LTO), and SKU-DiT trained under Reinforce-Distill-Reinforce (RDR) progressive training, conditioning jointly on the white-background product image, the trademark crop, and the structured layout. The authors also released CommerceGen-Bench, on which the framework scores highest overall among evaluated open-source baselines on both editing tracks and beats several larger closed-source models at Instruction Following and Product Integration.

Computer Science Computer Vision and Pattern Recognition Submitted on 14 Oct 2026 Title:CommerceGen-Studio: An End-to-End Framework for Commercial-Grade Main-Image Generation View PDF http://arxiv.org/pdf/2610.20603v1 HTML experimental https://arxiv.org/html/2610.20603v1 Abstract:We present CommerceGen-Studio, an end-to-end framework for generating commercial-grade main images for e-commerce Stock Keeping Units SKUs . Our framework is organized along a three-stage pipeline of information extraction, semantic rewriting, and multi-condition generation: Prompt-Eval-Refine-Loop Agent PERL-Agent distills heterogeneous SKU metadata into concise selling-point text lists through an iterative closed loop of VLM and LLM roles scored by a reference-free rubric. Layout Thinker Optimization LTO aligns the rewriter with both caption and rendered-image quality via alternating domain-specific policy optimization. SKU-DiT, trained under Reinforce-Distill-Reinforce RDR progressive training, conditions jointly on the white-background product image, the trademark crop, and the structured layout to hold subject fidelity, trademark integrity, and character-level text accuracy together. The framework runs in production at a scale of millions of main images per day with a latency of a few seconds per image, which we believe makes it among the first systems of this kind to operate at commercial scale. We also release CommerceGen-Bench, a benchmark of hard cases collected from live traffic across all product categories. On it, our framework achieves the highest overall scores among the evaluated open-source baselines on both editing tracks, with and without prompt enhancement. It also matches much larger closed-source models, and beats several of them at Instruction Following and Product Integration - the two properties that determine whether an image can be published. Additional Features References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .