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arXiv · 2601.18100

Spatial-Conditioned Reasoning in Long-Egocentric Videos

James Tribble·Hao Wang·Si-En Hong·Chaoyi Zhou·Ashish Bastola·Siyu Huang·Abolfazl Razi
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Abstract

Long-horizon egocentric video presents significant challenges for visual navigation due to viewpoint drift and the absence of persistent geometric context. Although recent vision-language models perform well on image and short-video reasoning, their spatial reasoning capability in long egocentric sequences remains limited. In this work, we study how explicit spatial signals influence VLM-based video understanding without modifying model architectures or inference procedures. We introduce Sanpo-D, a fine-grained re-annotation of the Google Sanpo dataset, and benchmark multiple VLMs on navigation-oriented spatial queries. To examine input-level inductive bias, we further fuse depth maps with RGB frames and evaluate their impact on spatial reasoning. Our results reveal a trade-off between general-purpose accuracy and spatial specialization, showing that depth-aware and spatially grounded representations can improve performance on safety-critical tasks such as pedestrian and obstruction detection.

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James Tribble, Hao Wang, Si-En Hong, Chaoyi Zhou, Ashish Bastola, Siyu Huang, Abolfazl Razi. 2026-01-26. Spatial-Conditioned Reasoning in Long-Egocentric Videos. https://arxiv.org/abs/2601.18100

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