New benchmark Animation2Code reveals VLM struggles with temporal video-to-code generation

A new benchmark called Animation2Code has been introduced to evaluate the temporal visual reasoning capabilities of vision-language models (VLMs) in generating code from videos. The benchmark comprises 1,069 web animation videos paired with their corresponding HTML/CSS/JavaScript implementations. Current state-of-the-art VLMs demonstrate significant challenges in maintaining temporal consistency during code reconstruction, even when achieving high visual fidelity. AI

IMPACT Highlights limitations in current vision-language models for tasks requiring temporal understanding, potentially guiding future research in video-to-code generation.

RANK_REASON The cluster describes a new academic paper introducing a benchmark and evaluation for AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Animation2Code
• arXiv
• HTML/CSS/JavaScript
• Vision--Language Models

• paper
• model release

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