8 papers
InfoDet: A Dataset for Infographic Element Detection
Jiangning Zhu, Yuxing Zhou, Zheng Wang +4
Given the central role of charts in scientific, business, and communication contexts, enhancing the chart understanding capabilities of vision-language models (VLMs) has become inc…
Demystifying Numerosity in Diffusion Models -- Limitations and Remedies
Yaqi Zhao, Xiaochen Wang, Li Dong +2
Numerosity remains a challenge for state-of-the-art text-to-image generation models like FLUX and GPT-4o, which often fail to accurately follow counting instructions in text prompt…
BizGen: Advancing Article-level Visual Text Rendering for Infographics Generation
Yuyang Peng, Shishi Xiao, Keming Wu +6
Recently, state-of-the-art text-to-image generation models, such as Flux and Ideogram 2.0, have made significant progress in sentence-level visual text rendering. In this paper, we…
MMMG: A Massive, Multidisciplinary, Multi-Tier Generation Benchmark for Text-to-Image Reasoning
Yuxuan Luo, Yuhui Yuan, Junwen Chen +6
In this paper, we introduce knowledge image generation as a new task, alongside the Massive Multi-Discipline Multi-Tier Knowledge-Image Generation Benchmark (MMMG) to probe the rea…
PrismLayers: Open Data for High-Quality Multi-Layer Transparent Image Generative Models
Junwen Chen, Heyang Jiang, Yanbin Wang +6
Generating high-quality, multi-layer transparent images from text prompts can unlock a new level of creative control, allowing users to edit each layer as effortlessly as editing t…
Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization
Zhanhao Liang, Yuhui Yuan, Shuyang Gu +5
Generating visually appealing images is fundamental to modern text-to-image generation models. A potential solution to better aesthetics is direct preference optimization (DPO), wh…