DVG-WM: Disentangled Video Generation Enables Efficient Embodied World Model for Robotic Manipulation

Video-based embodied world models provide an appealingsubstrate for robotic manipulation by predicting future states, yet cur-rent approaches remain limited by a fundamental entanglement: accu-rately modeling dynamics typically requires low-level temporal reason-ing, while producing high-resolution frames demands expansive visualsynthesis according to high-level semantics. This entanglement results inslow inference speed for iterative planning or too coarse predictions toretain contact-rich details. To solve this dilemma, we present Disentan-gled Video Generation World Model (DVG-WM), an efficient frameworkthat explicitly decomposes world modeling into dynamics learn-ing and visual synthesis. Conditioned on an initial observation anda language instruction, our model first generates a plausible sequence ofintermediate visual states to preview the physical interaction and refinesthem to obtain high-fidelity videos. Furthermore, an efficient cascadingmechanism is proposed, where DVG-WM leverages flow matching to di-rectly map the dynamics to video latents, and introduces a latent degra-dation mechanism to enable the capability of regenerating contact-richdetails. Experiments on LIBERO and real-world platforms demonstrateimproved video quality with up to 3.97 × acceleration, validating thatdisentangled video generation can be an efficient embodied world modelfor robotic manipulation.