MMSI-Video-Bench: A Holistic Benchmark for Video-Based Spatial Intelligence

Jingli Lin; Runsen Xu; Shaohao Zhu; Sihan Yang; Peizhou Cao; Yunlong Ran; Miao Hu; Chenming Zhu; Yiman Xie; Yilin Long; Wenbo Hu; Dahua Lin; Tai Wang; Jiangmiao Pang

arXiv · 2512.10863

MMSI-Video-Bench: A Holistic Benchmark for Video-Based Spatial Intelligence

Jingli Lin·Runsen Xu·Shaohao Zhu·Sihan Yang·Peizhou Cao·Yunlong Ran·Miao Hu·Chenming Zhu·Yiman Xie·Yilin Long·Wenbo Hu·Dahua Lin·Tai Wang·Jiangmiao Pang

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Abstract

Spatial understanding over continuous visual input is crucial for MLLMs to evolve into general-purpose assistants in physical environments. Yet there is still no comprehensive benchmark that holistically assesses the progress toward this goal. In this work, we introduce MMSI-Video-Bench, a fully human-annotated benchmark for video-based spatial intelligence in MLLMs. It operationalizes a four-level framework, Perception, Planning, Prediction, and Cross-Video Reasoning, through 1,106 questions grounded in 1,278 clips from 25 datasets and in-house videos. Each item is carefully designed and reviewed by 3DV experts with explanatory rationales to ensure precise, unambiguous grounding. Leveraging its diverse data sources and holistic task coverage, MMSI-Video-Bench also supports three domain-oriented sub-benchmarks (Indoor Scene Perception Bench, Robot Bench and Grounding Bench) for targeted capability assessment. We evaluate 25 strong open-source and proprietary MLLMs, revealing a striking human--AI gap: many models perform near chance, and the best reasoning model lags humans by nearly 60%. We further find that spatially fine-tuned models still fail to generalize effectively on our benchmark. Fine-grained error analysis exposes systematic failures in geometric reasoning, motion grounding, long-horizon prediction, and cross-video correspondence. We also show that typical frame-sampling strategies transfer poorly to our reasoning-intensive benchmark, and that neither 3D spatial cues nor chain-of-thought prompting yields meaningful gains. We expect our benchmark to establish a solid testbed for advancing video-based spatial intelligence.

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Jingli Lin, Runsen Xu, Shaohao Zhu, Sihan Yang, Peizhou Cao, Yunlong Ran, Miao Hu, Chenming Zhu, Yiman Xie, Yilin Long, Wenbo Hu, Dahua Lin, Tai Wang, Jiangmiao Pang. 2025-12-11. MMSI-Video-Bench: A Holistic Benchmark for Video-Based Spatial Intelligence. https://arxiv.org/abs/2512.10863

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