AI RESEARCH
Grounding Generated Videos in Feasible Plans via World Models
arXiv CS.LG
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ArXi:2602.01960v2 Announce Type: replace Large-scale video generative models have shown emerging capabilities as zero-shot visual planners, yet video-generated plans often violate temporal consistency and physical constraints, leading to failures when mapped to executable actions. To address this, we propose Grounding Video Plans with World Models (GVP-WM), a planning method that grounds video-generated plans into feasible action sequences using a learned action-conditioned world model.