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Towards Physics-Faithful Generation of Scientific Diagrams
Minghui Zhang, Jinxin Shi, Yifan Chang +12
Text-to-image generation has reached photorealistic quality, yet state-of-the-art systems remain unreliable at producing scientific diagrams, whose value depends not on appearance…
Faithful, Enriched, and Precise: Benchmarking Natural-Science Illustration Generation by T2I models
Yifan Chang, Jiaxin Ai, Jianwen Sun +9
Scientific illustrations are essential tools for communicating research findings, especially in natural science, where they visualize complex concepts and processes. As Text-to-Ima…
From Pixels to Paths: A Multi-Agent Framework for Editable Scientific Illustration
Jianwen Sun, Fanrui Zhang, Yukang Feng +6
Scientific illustrations demand both high information density and post-editability. However, current generative models have two major limitations: Frist, image generation models ou…
A High-Quality Dataset and Reliable Evaluation for Interleaved Image-Text Generation
Yukang Feng, Jianwen Sun, Chuanhao Li +8
Recent advancements in Large Multimodal Models (LMMs) have significantly improved multimodal understanding and generation. However, these models still struggle to generate tightly…
SridBench: Benchmark of Scientific Research Illustration Drawing of Image Generation Model
Yifan Chang, Yukang Feng, Jianwen Sun +4
Recent years have seen rapid advances in AI-driven image generation. Early diffusion models emphasized perceptual quality, while newer multimodal models like GPT-4o-image integrate…
IA-T2I: Internet-Augmented Text-to-Image Generation
Chuanhao Li, Jianwen Sun, Yukang Feng +3
Current text-to-image (T2I) generation models achieve promising results, but they fail on the scenarios where the knowledge implied in the text prompt is uncertain. For example, a…