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New benchmark Code2Video-Compass reveals MLLM struggles in code-based video generation

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Code2Video-Compass, a new benchmark designed to evaluate multimodal large language models (MLLMs) in code-based video generation. This benchmark covers text-to-video (T2V), image-to-video (I2V), and video-to-video (V2V) tasks, encompassing 2,104 scenarios across 10 categories and three complexity dimensions. Evaluations of 15 leading MLLMs indicate that even advanced models struggle with generating high-quality, complete videos that align with complex textual and visual requirements, especially as task complexity increases. AI

IMPACT This benchmark will help researchers identify and address limitations in MLLMs for complex video generation tasks.

RANK_REASON The cluster describes a new benchmark paper published on arXiv.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New benchmark Code2Video-Compass reveals MLLM struggles in code-based video generation

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhenghan Chen, Zekai Shao, Xin Lin, Leting Cheng, Siming Chen ·

    Code2Video-Compass: A Comprehensive Benchmark for General-Purpose Code-Based Video Generation across T2V, I2V, and V2V

    arXiv:2610.28416v1 Announce Type: cross Abstract: Code-based video generation offers fine-grained control, targeted editing, and component reuse compared with direct video generation. This approach requires multimodal large language models (MLLMs) to translate visual and temporal…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Code2Video-Compass: A Comprehensive Benchmark for General-Purpose Code-Based Video Generation across T2V, I2V, and V2V

    Code2Video-Compass is a benchmark evaluating multimodal language models on code-based video generation across text, image, and video inputs, revealing significant challenges in complex dynamic and state-dependent tasks.

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