ToG-Bench: Task-Oriented Spatio-Temporal Grounding in Egocentric Videos

Qi'ao Xu; Tianwen Qian; Yuqian Fu; Kailing Li; Yang Jiao; Jiacheng Zhang; Xiaoling Wang; Liang He

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arXiv · 2512.03666

ToG-Bench: Task-Oriented Spatio-Temporal Grounding in Egocentric Videos

Qi'ao Xu·Tianwen Qian·Yuqian Fu·Kailing Li·Yang Jiao·Jiacheng Zhang·Xiaoling Wang·Liang He

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Abstract

A core capability towards general embodied intelligence lies in localizing task-relevant objects from an egocentric perspective, formulated as Spatio-Temporal Video Grounding (STVG). Despite recent progress, existing STVG studies remain largely confined to object-centric and descriptive instructions, neglecting the task-oriented reasoning that is crucial for embodied agents to accomplish goal-directed interactions. To bridge this gap, we introduce \textbf{ToG-Bench}, the first task-oriented spatio-temporal video grounding benchmark for egocentric videos. ToG-Bench is characterized by three key features: (1) \textbf{Task-oriented Grounding}, which requires identifying and localizing objects based on intended tasks rather than straightforward descriptions; (2) \textbf{Explicit-Implicit Dual Grounding}, where target objects can be either explicitly mentioned or implicitly inferred by contextual reasoning; (3) \textbf{One-to-Many Grounding}, where a single instruction may correspond to multiple objects involved in task execution. Built upon videos sourced from ScanNet, ToG-Bench comprises 100 annotated clips with 2,704 task-oriented grounding instructions, constructed via a semi-automated pipeline that combines foundation model annotation and human refinement. In addition, we introduce a set of task-level evaluation metrics tailored for multi-object and explicit-implicit object grounding, and systematically benchmark seven state-of-the-art MLLMs. Extensive experiments reveal the intrinsic challenges of task-oriented STVG and substantial performance gaps across explicit-implicit and multi-object grounding, highlighting the difficulty of bridging perception and interaction in embodied scenarios. Data and code will be released at: \href{https://github.com/qaxuDev/ToG-Bench}{https://github.com/qaxuDev/ToG-Bench}..

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BibTeX RIS

Qi'ao Xu, Tianwen Qian, Yuqian Fu, Kailing Li, Yang Jiao, Jiacheng Zhang, Xiaoling Wang, Liang He. 2026-04-04. ToG-Bench: Task-Oriented Spatio-Temporal Grounding in Egocentric Videos. https://arxiv.org/abs/2512.03666

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