title: SridBench: Benchmark of Scientific Research Illustration Drawing of
    Image Generation Model
summary: 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 high-level reasoning, improving semantic
  understanding and structural composition. Scientific illustration generation
  exemplifies this evolution: unlike general image synthesis, it demands accurate
  interpretation of technical content and transformation of abstract ideas into
  clear, standardized visuals. This task is significantly more
  knowledge-intensive and laborious, often requiring hours of manual work and
  specialized tools. Automating it in a controllable, intelligent manner would
  provide substantial practical value. Yet, no benchmark currently exists to
  evaluate AI on this front. To fill this gap, we introduce SridBench, the first
  benchmark for scientific figure generation. It comprises 1,120 instances
  curated from leading scientific papers across 13 natural and computer science
  disciplines, collected via human experts and MLLMs. Each sample is evaluated
  along six dimensions, including semantic fidelity and structural accuracy.
  Experimental results reveal that even top-tier models like GPT-4o-image lag
  behind human performance, with common issues in text/visual clarity and
  scientific correctness. These findings highlight the need for more advanced
  reasoning-driven visual generation capabilities.
paper_authors._id: 683944793e5dd928f04e8431
paper_authors.name: Yifan Chang
paper_authors._id: 683944793e5dd928f04e8432
paper_authors.name: Yukang Feng
paper_authors._id: 683944793e5dd928f04e8433
paper_authors.name: Jianwen Sun
paper_authors._id: 683944793e5dd928f04e8434
paper_authors.name: Jiaxin Ai
paper_authors._id: 683944793e5dd928f04e8435
paper_authors.name: Chuanhao Li
paper_authors._id: 683944793e5dd928f04e8436
paper_authors.name: S. Kevin Zhou
paper_authors._id: 683944793e5dd928f04e8437
paper_authors.name: Kaipeng Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:46:49.196Z
paper_authors._id: 65f1713552c38a91e0a445e8
paper_authors.avatarUrl: /avatars/47ab3ada51c9b9976ac1cd0c4301c373.svg
paper_authors.fullname: kaipeng
paper_authors.type: user
paper_authors.user: kpzhang996
paper_title: SridBench: Benchmark of Scientific Research Illustration Drawing of
    Image Generation Model
paper_summary: 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 high-level reasoning, improving semantic
  understanding and structural composition. Scientific illustration generation
  exemplifies this evolution: unlike general image synthesis, it demands accurate
  interpretation of technical content and transformation of abstract ideas into
  clear, standardized visuals. This task is significantly more
  knowledge-intensive and laborious, often requiring hours of manual work and
  specialized tools. Automating it in a controllable, intelligent manner would
  provide substantial practical value. Yet, no benchmark currently exists to
  evaluate AI on this front. To fill this gap, we introduce SridBench, the first
  benchmark for scientific figure generation. It comprises 1,120 instances
  curated from leading scientific papers across 13 natural and computer science
  disciplines, collected via human experts and MLLMs. Each sample is evaluated
  along six dimensions, including semantic fidelity and structural accuracy.
  Experimental results reveal that even top-tier models like GPT-4o-image lag
  behind human performance, with common issues in text/visual clarity and
  scientific correctness. These findings highlight the need for more advanced
  reasoning-driven visual generation capabilities.
paper_ai_summary: The introduction of SridBench, a benchmark for scientific figure generation, reveals that current top-tier models, such as GPT-4o-image, fall short in semantic and structural accuracy compared to human performance, underscoring the need for more advanced multimodal reasoning-driven visual generation.
paper_ai_keywords: diffusion models multimodal models GPT-4o-image semantic understanding structural composition scientific illustration generation SridBench semantic fidelity structural accuracy