AI RESEARCH
ComPose: When to Trust Hands for Object Pose Tracking
arXiv CS.CV
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ArXi:2605.23523v1 Announce Type: new Reconstructing the motion of objects from videos is a key component for embodied AI and robot manipulation. While diverse approaches to object pose tracking have been studied, they rely heavily on strong external priors, such as depth data or 3D templates, and remain highly vulnerable to severe occlusions by hand grasps despite the use of explicit masks. In this work, we present ComPose, a 6DoF object tracking framework designed for hand-aware object pose estimation from RGB video.