SAVVY: Spatial Awareness via Audio-Visual LLMs through Seeing and Hearing
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918046553079808 |
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| author | Chen, Mingfei Cui, Zijun Liu, Xiulong Xiang, Jinlin Zheng, Caleb Li, Jingyuan Shlizerman, Eli |
| author_facet | Chen, Mingfei Cui, Zijun Liu, Xiulong Xiang, Jinlin Zheng, Caleb Li, Jingyuan Shlizerman, Eli |
| contents | 3D spatial reasoning in dynamic, audio-visual environments is a cornerstone of human cognition yet remains largely unexplored by existing Audio-Visual Large Language Models (AV-LLMs) and benchmarks, which predominantly focus on static or 2D scenes. We introduce SAVVY-Bench, the first benchmark for 3D spatial reasoning in dynamic scenes with synchronized spatial audio. SAVVY-Bench is comprised of thousands of relationships involving static and moving objects, and requires fine-grained temporal grounding, consistent 3D localization, and multi-modal annotation. To tackle this challenge, we propose SAVVY, a novel training-free reasoning pipeline that consists of two stages: (i) Egocentric Spatial Tracks Estimation, which leverages AV-LLMs as well as other audio-visual methods to track the trajectories of key objects related to the query using both visual and spatial audio cues, and (ii) Dynamic Global Map Construction, which aggregates multi-modal queried object trajectories and converts them into a unified global dynamic map. Using the constructed map, a final QA answer is obtained through a coordinate transformation that aligns the global map with the queried viewpoint. Empirical evaluation demonstrates that SAVVY substantially enhances performance of state-of-the-art AV-LLMs, setting a new standard and stage for approaching dynamic 3D spatial reasoning in AV-LLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05414 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SAVVY: Spatial Awareness via Audio-Visual LLMs through Seeing and Hearing Chen, Mingfei Cui, Zijun Liu, Xiulong Xiang, Jinlin Zheng, Caleb Li, Jingyuan Shlizerman, Eli Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multimedia Sound Audio and Speech Processing 3D spatial reasoning in dynamic, audio-visual environments is a cornerstone of human cognition yet remains largely unexplored by existing Audio-Visual Large Language Models (AV-LLMs) and benchmarks, which predominantly focus on static or 2D scenes. We introduce SAVVY-Bench, the first benchmark for 3D spatial reasoning in dynamic scenes with synchronized spatial audio. SAVVY-Bench is comprised of thousands of relationships involving static and moving objects, and requires fine-grained temporal grounding, consistent 3D localization, and multi-modal annotation. To tackle this challenge, we propose SAVVY, a novel training-free reasoning pipeline that consists of two stages: (i) Egocentric Spatial Tracks Estimation, which leverages AV-LLMs as well as other audio-visual methods to track the trajectories of key objects related to the query using both visual and spatial audio cues, and (ii) Dynamic Global Map Construction, which aggregates multi-modal queried object trajectories and converts them into a unified global dynamic map. Using the constructed map, a final QA answer is obtained through a coordinate transformation that aligns the global map with the queried viewpoint. Empirical evaluation demonstrates that SAVVY substantially enhances performance of state-of-the-art AV-LLMs, setting a new standard and stage for approaching dynamic 3D spatial reasoning in AV-LLMs. |
| title | SAVVY: Spatial Awareness via Audio-Visual LLMs through Seeing and Hearing |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multimedia Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.05414 |