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

Zhenghan Chen, Zekai Shao, Xin Lin, Leting Cheng, Siming Chen · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28416 · cited by 0 works (OpenAlex) · open-access PDF

Korean

코드 기반 애니메이션 비디오 생성도 완전한 코드 기반 비디오 생성에 대한 완전한 시각적 요구 사항을 번역하기 위해 멀티모델 대형 언어 모델 (MLLM)을 필요로합니다. 그러나 기존 코드 기반 비디오 벤치마크는 주로 전문 작업을 대상으로하며 다양한 창조적 인 범주와 입력 모드에 대한 완전한 비디오 생성은 충분히 평가되지 않습니다. Code2Video-Compass는 코드 기반 비디오 생성 (T2V), 이미지-비디오 (I2V) 및 비디오-비디오 (V2V) 설정에 대한 기존 코드 기반 벤치마크를 완전한 비디오 생성에 대한 완전한 시각적 요구 사항을 번역합니다. Code2Video-Compass는 10 개의 범주, 24 하위 유형,

Machine translation (Meta M2M100); the English original follows.

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 requirements into executable programs that produce complete videos. However, existing code-based benchmarks primarily target specialized tasks, leaving complete video creation across diverse creative categories and input modalities insufficiently evaluated. We introduce Code2Video-Compass, a general-purpose benchmark for code-based video generation across text-to-video (T2V), image-to-video (I2V), and video-to-video (V2V) settings. Code2Video-Compass comprises 2,104 tasks spanning 10 categories, 24 subtypes, and three complexity dimensions covering construction, dynamic animation, and state dependency. It combines tasks curated from textual instructions and visual references with corresponding T2V, I2V, and V2V tasks derived from complete professional and user-created videos. We propose a dual-level evaluation framework combining 12 objective metrics and five subjective metrics. Objective evaluation covers operational performance, video quality, text-video alignment, and reference fidelity, while subjective evaluation uses an MLLM judge to evaluate instruction following and holistic video quality. Extensive evaluation of 15 recent proprietary and open-weight MLLMs from nine families reveals that even leading models face challenges in reliably generating high-quality complete videos aligned with textual and visual requirements, with performance generally declining as construction, dynamic animation, and state-dependency complexity increases. We anticipate that Code2Video-Compass will advance reliable, coherent, and controllable code-based video generation and inspire broader progress in programmatic visual creation.

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Topics: Computer science, Benchmark (surveying), Executable, Modalities, Component (thermodynamics), Modality (human–computer interaction)

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