VCN-Bench：事前視覚経験に基づく空間推論のためのビデオ文脈化ナビゲーションベンチマーク
VCN-Bench: A Video-Contextualized Navigation Benchmark for Spatial Reasoning over Prior Visual Experience
事前に撮影された動画を手がかりに、指示された目的地を推論してナビゲーションする新ベンチマークVCN-Benchを提案し、MLLMの閉ループ空間推論能力を評価した。
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著者: Siqi Zhang, Meng Wei, Chenyang Wan, Shaohao Zhu, Shufan Shen, Xihui Liu, Zhihua Wei, Tai Wang, Jiangmiao Pang
分類: cs.AI
原文アブストラクト
Spatial reasoning is fundamental to embodied agents, yet it remains unclear whether spatial understanding can be carried forward to guide sequential interactions. Existing spatial-reasoning benchmarks typically terminate at offline predictions, while navigation benchmarks evaluate spatial reasoning as part of instruction following and exploration. We introduce VCN-Bench, a \textbf{V}ideo-\textbf{C}ontextualized \textbf{N}avigation benchmark for probing closed-loop spatial reasoning over prior visual experience in MLLMs. Given a prior video covering both the initial location and destination, the agent is tasked with reasoning out the instruction-specified target and navigating toward it with the inferred spatial context. Built on Matterport3D, VCN-Bench contains five instruction types, 100k training episodes, and 1,250 evaluation episodes. Navigation serves as the primary evaluation, while diagnostic goal identification helps distinguish destination-resolution errors from subsequent navigation failures. We further propose MV-DualVLN, a planning-oriented baseline that jointly leverages prior video and in-episode observations. Experiments reveal limited navigation performance, a substantial destination-resolution-to-navigation gap, and frequent navigation failures even after correct destination identification.
