HAT-4D: Lifting Monocular Video for 4D Multi-Object Interactions via Human-Agent Collaboration
Evolving story · 1 updatesHAT-4D: Multi-Object 4D Reconstruction FrameworkTimeline →Researchers propose HAT-4D, a novel agentic framework using VLMs to reconstruct 3D geometry, temporal dynamics, and physical interactions of multiple objects from a single monocular video, addressing occlusions and complex dynamics in multi-object interactions.
Extracting dynamic 4D object interactions from massive, in-the-wild monocular videos offers a highly efficient data collection pathway for scaling Embodied AI and training VLAs. However, existing monocular 4D reconstruction methods primarily focus on isolated objects, often failing under the severe occlusions and complex dynamics inherent in multi-object interactions. To bridge this gap, we propose HAT-4D, the first agentic framework designed to reconstruct the 3D geometry, temporal dynamics, and physical interactions of multiple objects from a single video. By integrating VLMs with a multi-le
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