CommerceGen-Studio: An End-to-End Framework for Commercial-Grade Main-Image Generation
We present CommerceGen-Studio, an end-to-end framework for generating commercial-grade main images for e-commerce Stock Keeping Units (SKUs).
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Key points
- 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.
- We also release CommerceGen-Bench, a benchmark of hard cases collected from live traffic across all product categories.
Sources (2)
- [1]CommerceGen-Studio: An End-to-End Framework for Commercial-Grade Main-Image GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 05:59 PM
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.
- [2]CommerceGen-Studio: An End-to-End Framework for Commercial-Grade Main-Image GenerationHugging Face Daily Papers · Oct 14, 12:00 AM · same content
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