← 研踪 PaperTrailCVF · CVPR 2026:cvf:051c939fbf9b7209f3462c00d6a505c8 · 记录 #1
Thinking in 360deg: Humanoid Visual Search in the Wild
Yu, HeyangHan, YinanZhang, XiangyuYin, BaiqiaoChang, BowenHan, XiangyuLiu, XinhaoZhang, JingPavone, MarcoFeng, ChenXie, SainingLi, Yiming 来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 23:32
摘要
Humans rely on the synergistic control of head (cephalomotor) and eye (oculomotor) to efficiently search for visual information in 360deg. However, prior approaches to visual search are limited to a static image, neglecting the physical embodiment and its interaction with the 3D world. How can we develop embodied visual search agents as efficient as humans while bypassing the constraints imposed by real-world hardware? To this end, we propose humanoid visual search where a humanoid agent actively rotates its head to search for objects or paths in an immersive world represented by a 360deg panoramic image. To study visual search in visually-crowded real-world scenarios, we build H* Bench, a new benchmark that moves beyond household scenes to challenging in-the-wild scenes that necessitate advanced visual-spatial reasoning capabilities, such as transportation hubs, large-scale retail spaces, urban streets, and public institutions. Our experiments first reveal that even top-tier proprietary models falter, achieving only 30% success in object and path search. We then use post-training techniques to enhance the open-source models, increasing its success rate by over threefold for both object search (14.83% - 47.38%) and path search (6.44% - 24.94%) on the smallest 3B model. Notably, the lower ceiling of path search reveals its inherent difficulty, which we attribute to the demand for sophisticated spatial commonsense. Our results not only show a promising path forward but also quantify the immense challenge that remains in building MLLM agents that can be seamlessly integrated into everyday human life.
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记录 #1 · 2026 · 最新观测版本
Thinking in 360deg: Humanoid Visual Search in the Wild
Humans rely on the synergistic control of head (cephalomotor) and eye (oculomotor) to efficiently search for visual information in 360deg. However, prior approaches to visual search are limited to a static image, neglecting the physical embodiment and its interaction with the 3D world. How can we develop embodied visual search agents as efficient as humans while bypassing the constraints imposed by real-world hardware? To this end, we propose humanoid visual search where a humanoid agent actively rotates its head to search for objects or paths in an immersive world represented by a 360deg panoramic image. To study visual search in visually-crowded real-world scenarios, we build H* Bench, a new benchmark that moves beyond household scenes to challenging in-the-wild scenes that necessitate advanced visual-spatial reasoning capabilities, such as transportation hubs, large-scale retail spaces, urban streets, and public institutions. Our experiments first reveal that even top-tier proprietary models falter, achieving only 30% success in object and path search. We then use post-training techniques to enhance the open-source models, increasing its success rate by over threefold for both object search (14.83% - 47.38%) and path search (6.44% - 24.94%) on the smallest 3B model. Notably, the lower ceiling of path search reveals its inherent difficulty, which we attribute to the demand for sophisticated spatial commonsense. Our results not only show a promising path forward but also quantify the immense challenge that remains in building MLLM agents that can be seamlessly integrated into everyday human life.
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