Preprint
Aug 2026
LD4WAM: Learning Latent Dynamics from Human Videos for World Action Models
LD4WAM is presented, which pairs a Latent Dynamics Model trained with semantic reconstruction and real motion alignment with a World Dynamics Action Model built as a mixture-of-transformers (MoT), which preserves full future-video generation and uses learnable queries to distill these latent dynamics from generated futures for action conditioning.
Zhen Shen, Jiaqi Liang, Jasper Lu et al.
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