From Pixel to Coding: Evaluating the Figure Reproduction Capabilities of MLLMs
To bridge this gap, we propose FigCodeBench, a comprehensive framework for rigorously evaluating MLLMs on figure reproduction, integrating multimodal comprehension and generation.
Key points
- Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in both visual understanding and code generation.
- Moreover, current visual code generation benchmarks often rely on simplified layouts within single programming environments, falling short of evaluating true unified multimodal reasoning.
- We further categorize figure reproduction into three tiers with visual and code complexity modeling, specifically targeting complex structural reasoning, varying aspect ratios, and dense geometric constraints.
- We introduce a multi-dimensional evaluation protocol, encompassing visual fidelity and syntactic isomorphism, that aligns highly with the Mean Machine Opinion Score (MMOS) and human preferences.
Sources (1)
- [1]From Pixel to Coding: Evaluating the Figure Reproduction Capabilities of MLLMsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 01:33 PM
To bridge this gap, we propose FigCodeBench, a comprehensive framework for rigorously evaluating MLLMs on figure reproduction, integrating multimodal comprehension and generation.
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in both visual understanding and code generation.
Extractive summary: sentences quoted from the sources.