DreamX: Video Diffusion Model is a Universal Latent Action Learner

JisooKim, Chaehyun Kim, Junsu Kim, Jiho Park, Seongchan Kim, Eunbeen Hong, Soowon Son, Fu-En Yang, Seungryong Kim

DreamX: Video Diffusion Model is a Universal Latent Action Learner: 46 upvotes on Hugging Face Daily Papers, #6 of 46 papers on 2026-11-04. Day-by-day upvote history.

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It had 46 upvotes by its first day, 46 upvotes by the next day, 46 upvotes by day 3, 46 upvotes by day 7, and has 46 now. It ranked #13 of 130 papers in its week and #13 of 130 in its month.

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 addition, by leveraging the video model's generative prior, these latent actions capture action-relevant dynamics disentangled from visual appearance. The resulting latent space serves as a shared action interface across embodiments (human and robot), environments, and action parameterizations. On LARY-Bench, DreamX achieves state-of-the-art performance among latent action models. Moreover, actions encoded in one setting remain effective in another: the latent space supports any-to-any video transfer and zero-shot action transfer across diverse embodiments and environments. Furthermore, this shared interface also enables policy learning in a compact latent action space, reducing the computational cost of video-diffusion-based policies.

Paper page on Hugging Face · arXiv

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