The paper investigates how rendered text in image generation models can leak semantic information, acting as an unintended control channel. By separating the visual prompt from the text, the authors measure how text semantics influence generated images and show that harmful meanings can persist even when the main prompt is benign. They also propose a mitigation strategy that limits unsafe semantic transfer while maintaining correct text rendering.
By Feifei Li, Runjie Wang, Xiaohan Zhang, Zhenxing Qian, Mi Wen, Mi Zhang
The paper introduces Orthogonal Drift Correction (ODC), an inference-time guidance method that mitigates semantic drift in text-to-image generation without retraining or extra user input. ODC operates in two stages: first it detects drift by comparing the generated image to the prompt in a shared vision-language embedding space, isolates the orthogonal component of the drift, and converts it back into text; second it refines the text conditioning for a second generation pass using an adaptive rank-reduced concept removal module. Experiments show ODC improves prompt-image alignment, raising CLIP scores by up to 5.4% on COALBench-300 and PartiPrompts, and boosting the 3-in-1 score by 4.5% on T2I-CompBench’s complex subset.
By Kasra Kamali, Basim Azam, Tuan Duong Trinh, Jean Honorio, Naveed Akhtar
arXiv:2609.36598v1 Announce Type: new
Abstract: A video can exhibit convincing motion and photorealism yet fail immediately when visual text collapses. Unlike generic scene content, visual text is un...
By Ziying Zhang, Litao Li, Junchao Liao, Tianyi Zeng, Siyu Zhu, Long Qin, Zhenghao Zhang
VTR-Bench is a new benchmark designed to evaluate how well video generation models render text within scenes. It includes 300 prompts across five real-world scenarios such as advertisements and scientific videos, and uses an automated pipeline with human alignment to assess text fidelity and scene/motion requirements. Experiments on 11 state‑of‑the‑art models show that even the best performer has a word error rate of 0.250, underscoring widespread challenges in visual text rendering.
By Yu Huang, Jungang Li, Zhiyuan Wang, Yonghua Hei, Song Dai, Jiayu Yang, Deyuan Liu, Xiang Zheng, Xiaoshuang Shi, Hao Cheng, Kaidi Xu
TextVBench is a new benchmark and evaluation framework for assessing how well video generation models render and preserve text. It introduces two tracks—TextVBench‑T2V for prompt‑specified text creation and maintenance, and TextVBench‑Ref2V for preserving text from reference images—using a multi‑target, multi‑instance spatiotemporal correspondence approach. The framework employs target‑conditioned candidate selection, supports multiple OCR regions per target, and separates spatial and temporal fidelity, summarizing results with a length‑weighted harmonic score and detailed diagnostics.
By Ruiqiang Zhang, Hengyi Wang, Chang Liu, Guanjie Wang, Zehua Ma, Weiming Zhang
arXiv:2505. 16915v3 Announce Type: replace-cross Abstract: While recent Text-to-Image (T2I) models show impressive capabilities in synthesizing images from brief descriptions, they struggle with the long, detailed prompts required for professional applications.
By Qirui Jiao, Daoyuan Chen, Yilun Huang, Xika Lin, Ying Shen, Yaliang Li