EgoProx: 認知階層にわたる自己中心3D近接推論におけるMLLMの評価
EgoProx: Evaluating MLLMs on Egocentric 3D Proximity Reasoning Across a Cognitive Hierarchy
自己中心視点での3D近接推論を評価するベンチマークEgoProxを提案し、多様なQAペアを生成するデータエンジンを設計。既存のMLLMの性能を分析し、空間知識はあるものの推論には課題があることを示した。
著者: Jinzhao Li, Yinuo Chen, Dongxu Piao, Panwang Pan, Yifan Yu, Dong Wang, Honglei Yan, Liang Yue, Shaofei Wang, Yixin Chen, Siyuan Huang, Miao Liu
分類: cs.CV
原文アブストラクト
Humans constantly reason about 3D proximity, the relations between their body and surrounding objects, to guide perception and action in daily life. Whether multimodal large language models (MLLMs) can perform such embodied 3D reasoning remains unclear. To this end, we introduce EgoProx, a benchmark for egocentric 3D proximity reasoning. We organize our tasks along a cognitive chain, covering intention, exploration, exploitation, and chain-of-actions reasoning. We also design an agent based data engine that produces diverse and consistent QA pairs at scale. We benchmark prevailing MLLMs on EgoProx and conduct additional analyses with dataset specific and task specific instruction tuning. We observe large cross-domain gains, indicating that current MLLMs contain some spatial knowledge; however, they still struggle to effectively leverage it for spatial reasoning VQA.