# TransAnyText: Translating Arbitrary Text in E-commerce Images via Structured Visual Generation

arXiv: https://arxiv.org/abs/2608.16284 · HTML: https://mlsanity.com/paper/2608.16284/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 36 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 26 of 26)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Qwen-Image Technical Report](https://mlsanity.com/paper/2508.02324/) (2025)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space](https://mlsanity.com/paper/2506.15742/) (2025)
- [Seedream 4.0: Toward Next-generation Multimodal Image Generation](https://mlsanity.com/paper/2509.20427/) (2025)
- [AnyText: Multilingual Visual Text Generation And Editing](https://mlsanity.com/paper/2311.03054/) (2023)
- [StarVector: Generating Scalable Vector Graphics Code from Images and Text](https://mlsanity.com/paper/2312.11556/) (2023)
- [LongCat-Image Technical Report](https://mlsanity.com/paper/2512.07584/) (2025)
- [PosterMaker: Towards High-Quality Product Poster Generation with Accurate Text Rendering](https://mlsanity.com/paper/2504.06632/) (2025)
- [FLUX-Text: A Simple and Advanced Diffusion Transformer Baseline for Scene Text Editing](https://mlsanity.com/paper/2505.03329/) (2025)
- [TextCtrl: Diffusion-based Scene Text Editing with Prior Guidance Control](https://mlsanity.com/paper/2410.10133/) (2024)
- [Visual Text Processing: A Comprehensive Review and Unified Evaluation](https://mlsanity.com/paper/2504.21682/) (2025)
- [First Creating Backgrounds Then Rendering Texts: A New Paradigm for Visual Text Blending](https://mlsanity.com/paper/2410.10168/) (2024)
- [Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation](https://mlsanity.com/paper/2407.02894/) (2024)
- [CharGen: High Accurate Character-Level Visual Text Generation Model with MultiModal Encoder](https://mlsanity.com/paper/2412.17225/) (2024)
- [GenClaw: Code-Driven Agentic Image Generation](https://mlsanity.com/paper/2605.30248/) (2026)
- [PosterVerse: A Full-Workflow Framework for Commercial-Grade Poster Generation with HTML-Based Scalable Typography](https://mlsanity.com/paper/2601.03993/) (2026)
- [AutoPP: Towards Automated Product Poster Generation and Optimization](https://mlsanity.com/paper/2512.21921/) (2025)
- [PRIM: Towards Practical In-Image Multilingual Machine Translation](https://mlsanity.com/paper/2509.05146/) (2025)
- [InnoAds-Composer: Efficient Condition Composition for E-Commerce Poster Generation](https://mlsanity.com/paper/2603.05898/) (2026)
- [Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence](https://mlsanity.com/paper/2606.15932/) (2026)
- [DesignAsCode: Bridging Structural Editability and Visual Fidelity in Graphic Design Generation](https://mlsanity.com/paper/2602.17690/) (2026)
- [Global-Local Dual Perception for MLLMs in High-Resolution Text-Rich Image Translation](https://mlsanity.com/paper/2602.21956/) (2026)
- [GMO-E2DIT: Grounded Multi-Operation Editing for E-Commerce Images](https://mlsanity.com/paper/2607.00920/) (2026)
- [UniTranslator: A Unified Multi-modal Framework for End-to-end In-Image Machine Translation](https://mlsanity.com/paper/2606.24333/) (2026)

## Built on by (most cited first, 1 of 1)

- [CommerceGen-Studio: An End-to-End Framework for Commercial-Grade Main-Image Generation](https://mlsanity.com/paper/2610.20603/) (2026)

## Abstract (arXiv)

Cross-border e-commerce image translation is essential for global retail, where product images, banners, and detail pages need to be produced in different languages. Existing methods struggle to achieve accurate translation, faithful visual identity preservation, and easy-to-edit outputs, simultaneously. To address these challenges, we introduce TransAnyText, a structured visual code framework that reformulates image text translation as generating renderable HTML patches from source images and target languages. Our framework decouples semantic generation from pixel rendering: a vision-language model (VLM) handles visual understanding, cross-lingual translation, and structured visual generation, while a diffusion model performs background inpainting and pixel-level refinement, followed by deterministic rendering to synthesize the final image. Based on this formulation, we develop a three-stage post-training framework, where supervised fine-tuning (SFT) establishes the image-to-code mapping, privilege-gap weighted self-distillation (PWSD) improves the learning of style and layout tokens, and reinforcement learning with verifiable rewards (RLVR) further optimizes task-level performance. We further introduce TransAnyDataset and TransAnyBench, a multilingual dataset and benchmark for e-commerce image translation. Extensive experiments demonstrate competitive performance against cascaded pipelines, open-source end-to-end models, and closed-source image editing systems, providing an effective, controllable, and editable solution for cross-border e-commerce image translation.