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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2603.07966 |
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| _version_ | 1866911498612244480 |
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| author | Zhou, Weijie Xiong, Xuantang Hu, Zhenlin Zhu, Xiaomeng Zhao, Chaoyang Dong, Honghui Zhang, Zhengyou Tang, Ming Wang, Jinqiao |
| author_facet | Zhou, Weijie Xiong, Xuantang Hu, Zhenlin Zhu, Xiaomeng Zhao, Chaoyang Dong, Honghui Zhang, Zhengyou Tang, Ming Wang, Jinqiao |
| contents | In situated collaboration, speakers often use intentionally underspecified deictic commands (e.g., ``pass me \textit{that}''), whose referent becomes identifiable only by aligning speech with a brief co-speech pointing \emph{stroke}. However, many embodied benchmarks admit language-only shortcuts, allowing MLLMs to perform well without learning the \emph{audio--visual alignment} required by deictic interaction. To bridge this gap, we introduce \textbf{Egocentric Co-Speech Grounding (EcoG)}, where grounding is executable only if an agent jointly predicts \textit{What}, \textit{Where}, and \textit{When}. To operationalize this, we present \textbf{EcoG-Bench}, an evaluation-only bilingual (EN/ZH) diagnostic benchmark of \textbf{811} egocentric clips with dense spatial annotations and millisecond-level stroke supervision. It is organized under a \textbf{Progressive Cognitive Evaluation} protocol. Benchmarking state-of-the-art MLLMs reveals a severe executability gap: while human subjects achieve near-ceiling performance on EcoG-Bench (\textbf{96.9\%} strict Eco-Accuracy), the best native video-audio setting remains low (Gemini-3-Pro: \textbf{17.0\%}). Moreover, in a diagnostic ablation, replacing the native video--audio interface with timestamped frame samples and externally verified ASR (with word-level timing) substantially improves the same model (\textbf{17.0\%}$\to$\textbf{42.9\%}). Overall, EcoG-Bench provides a strict, executable testbed for event-level speech--gesture binding, and suggests that multimodal interfaces may bottleneck the observability of temporal alignment cues, independently of model reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07966 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Listening with the Eyes: Benchmarking Egocentric Co-Speech Grounding across Space and Time Zhou, Weijie Xiong, Xuantang Hu, Zhenlin Zhu, Xiaomeng Zhao, Chaoyang Dong, Honghui Zhang, Zhengyou Tang, Ming Wang, Jinqiao Computer Vision and Pattern Recognition In situated collaboration, speakers often use intentionally underspecified deictic commands (e.g., ``pass me \textit{that}''), whose referent becomes identifiable only by aligning speech with a brief co-speech pointing \emph{stroke}. However, many embodied benchmarks admit language-only shortcuts, allowing MLLMs to perform well without learning the \emph{audio--visual alignment} required by deictic interaction. To bridge this gap, we introduce \textbf{Egocentric Co-Speech Grounding (EcoG)}, where grounding is executable only if an agent jointly predicts \textit{What}, \textit{Where}, and \textit{When}. To operationalize this, we present \textbf{EcoG-Bench}, an evaluation-only bilingual (EN/ZH) diagnostic benchmark of \textbf{811} egocentric clips with dense spatial annotations and millisecond-level stroke supervision. It is organized under a \textbf{Progressive Cognitive Evaluation} protocol. Benchmarking state-of-the-art MLLMs reveals a severe executability gap: while human subjects achieve near-ceiling performance on EcoG-Bench (\textbf{96.9\%} strict Eco-Accuracy), the best native video-audio setting remains low (Gemini-3-Pro: \textbf{17.0\%}). Moreover, in a diagnostic ablation, replacing the native video--audio interface with timestamped frame samples and externally verified ASR (with word-level timing) substantially improves the same model (\textbf{17.0\%}$\to$\textbf{42.9\%}). Overall, EcoG-Bench provides a strict, executable testbed for event-level speech--gesture binding, and suggests that multimodal interfaces may bottleneck the observability of temporal alignment cues, independently of model reasoning. |
| title | Listening with the Eyes: Benchmarking Egocentric Co-Speech Grounding across Space and Time |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.07966 |