Learning a universal action representation from video requires modeling dynamics that are consistent across different executions of the same action yet invariant to changes in embodiment and appearanc
DreamX leverages a pretrained video diffusion model to learn compact, appearance-invariant latent actions that enable cross-embodiment transfer and efficient policy learning.
Learning a universal action representation from video requires modeling dynamics that are consistent across different executions of the same action yet invariant to changes in embodiment and appearance. Existing latent action models infer actions from visual transitions, but their representations often entangle the underlying action with irrelevant visual variations, limiting transfer across embodiments. We introduce DreamX, which repurposes a pretrained video diffusion model as a universal latent action learner. Our approach extracts compact latent actions from the video model's rich spatiotemporal features, compressing each latent video frame by 300times (1{,200 rightarrow 4} tokens). In…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
DreamX: Video Diffusion Model is a Universal Latent Action Learner was first seen on Hugging Face · Daily papers on Nov 4, 2026. Since then it has appeared in 16 checks on 2 different days. Its best position was #2 of 30, reached on Nov 5, 07:28 UTC. It dropped off the list after Nov 5, 13:28 UTC.
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#2 on Hugging Face · Daily papers, on Nov 5, 07:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 4, 2026 and has been on the list on 2 days (16 checks) since.