arXiv Computer Vision
Sep 3

GlyphAnchor: Enhancing Visual Text Rendering via Position-Anchored Glyph Priors

GlyphAnchor is a new method that improves visual text rendering in image generation and editing models by adding lightweight glyph patch conditions anchored to the target image’s positional encoding. The approach is trained with staged supervised finetuning and text-aware post‑training, and it works with both text‑to‑image and image‑editing diffusion transformers. Experiments on various backbones and the newly introduced InfoTextBench benchmark show that GlyphAnchor consistently enhances text fidelity while maintaining overall image quality, especially for long, complex, or densely arranged text and rare characters.

By Qiang Xiang, Shuang Sun, Binglei Li, Yibo Chen, Xu Tang, Yao Hu, Junping Zhang
arXiv Computer Vision
Oct 2

VTR-Bench: A Systematic Benchmark for Evaluating Visual Text Rendering in Video Generation

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
arXiv Computer Vision
Aug 21

TextRefine: Improving Textual Fidelity, Spatial Placement, and Glyph Rendering for Text Editing in Product Posters

arXiv:2608. 19637v1 Announce Type: new Abstract: Text editing in product posters entails inserting new text or replacing existing text while preserving product appearance, background content, and global composition.

By Honglie Wang, Jia Sun, Zijun Li, Junlong Wu, Pengcheng Wei, Jiyuan Wang, Yongrui Heng, Boheng Zhang, Huaiqing Wang, Dewen Fan, Qianqian Gan, Fan Yang, Tingting Gao, Yan-Ming Zhang
arXiv Computer Vision
Sep 21

Edit-VAR: Taming Visual Autoregressive Model for Precise Video Editing

Edit‑VAR is a training‑free, inversion‑free framework that uses a pretrained visual autoregressive video model for text‑guided video editing. It encodes the source video into multi‑scale discrete tokens and applies probability‑guided conditional token replacement, attention‑guided token‑wise and scale‑aware modulation, and scale‑decoupled generation to preserve source appearance while enabling precise edits. The method also includes residual‑guided token pruning to reduce inference cost, and experimental results show it outperforms existing training‑free video editing methods in fidelity, source preservation, temporal coherence, and efficiency.

By Chongbo Zhao, Jiangming Wang, Xilai Wang, Xinyu Wang, Jingyi Tang, Chunjie Hao, Pengjie Song, Yue Ma