LIME: Learning Intent-aware Camera Motion from Egocentric Video
Autonomous robots often need to move their camera before they can act: to inspect an object, reveal an occluded region, or obtain a view that responds to a user's intent. While vision-language navigation translates instructions to base motion and vision-language-action policies map instructions to manipulation actions, language-conditioned camera motion remains comparatively underexplored as a first-class action. We formulate language-conditioned camera motion generation: given a current RGB observation and a free-form natural-language intent, predict a relative target camera pose for the next
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- PossiblePossibly related (embedding) · 52%VioletVision-3B →
- LinkedLinked via arxiv author · 85%Boyang Sun →
“LIME: Learning Intent-aware Camera Motion from Egocentric Video”
- LinkedLinked via arxiv author · 85%Jiajie Li →
“LIME: Learning Intent-aware Camera Motion from Egocentric Video”
- LinkedLinked via arxiv author · 85%Yung-Hsu Yang →
“LIME: Learning Intent-aware Camera Motion from Egocentric Video”
- LinkedLinked via arxiv author · 85%Chenyangguang Zhang →
“LIME: Learning Intent-aware Camera Motion from Egocentric Video”
- LinkedLinked via arxiv author · 85%Tim Engelbracht →
“LIME: Learning Intent-aware Camera Motion from Egocentric Video”
- LinkedLinked via arxiv author · 85%Sunghwan Hong →
“LIME: Learning Intent-aware Camera Motion from Egocentric Video”
- LinkedLinked via arxiv author · 85%Cesar Cadena →
“LIME: Learning Intent-aware Camera Motion from Egocentric Video”