Efficient Scene-Text Refinement via Pixel-Space Grouped Image Generation
In this work, we introduce PixText, a model that formulates local text refinement as grouped image generation in pixel space.
ProofPaper ↗
Key points
- Generated images often have coherent layouts and visual content, yet local errors in small text and complex characters still limit their readability and practical use.
- Scene-text refinement requires accuracy, preservation, and efficiency.
- Conditioned on the original image, PixText generates only the target regions, preserves unedited content, and uses MeanFlow for four-step generation.
- We further introduce Text Refinement Bench to evaluate text correction and image preservation in generated images.
Sources (2)
- [1]Efficient Scene-Text Refinement via Pixel-Space Grouped Image GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 12:34 PM
In this work, we introduce PixText, a model that formulates local text refinement as grouped image generation in pixel space.
Generated images often have coherent layouts and visual content, yet local errors in small text and complex characters still limit their readability and practical use.
- [2]Efficient Scene-Text Refinement via Pixel-Space Grouped Image GenerationHugging Face Daily Papers · Oct 13, 12:00 AM · same content
Extractive summary: sentences quoted from the sources.
Before this
- Oct 12, 2026FORGE: Procedural Weight Generation for Sparse MoE Inference
- Oct 12, 2026Image Tokenizers Should Be Trained Between Images
- Oct 12, 2026DuoDiscover: Scaling Autonomous Discovery Beyond the Evaluation Bottleneck
- Oct 11, 2026AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards
- Oct 11, 2026Imperfect Verifiers, Exact Targets: Inference-Time Scaling for Continuous Generation
- Oct 11, 2026Data-Centric Recursive Self-Improvement for Navigation via Policy-Synchronized Language-Action Dual Bootstrapping
