DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation

We present $\mathbf{\text{DreamX-Phi}}\mathbf{\text{1.0}}$, an action-conditioned video world model for robotic manipulation that, given an observed frame, a language instruction, and a prescribed action sequence comprising end-effector poses and gripper states, predicts the resulting future observations. Yet realism alone does not guarantee faithfulness: a convincing rollout can still move the wrong arm or lose the manipulated object. To ensure the prediction respects each arm’s commanded path, we inject per-arm $\mathrm{S}\mathrm{E}(3)$ transformations into attention via $\mathbf{\text{PRoPE-style}}\mathbf{\text{geometric}}\mathbf{\text{encoding}}$, preserving arm identity and rigid-motion structure. Action control alone does not fully constrain scene geometry or the evolution of small manipulated objects. We therefore add a lightweight $\mathbf{\text{depth}}\mathbf{\text{branch}}$ for scene-level geometry and use $\mathbf{\text{SAM3}}\mathbf{\text{masks}}$ with a frozen $\mathbf{\text{V-JEPA}}\mathbf{\text{teacher}}$ to maintain object consistency throughout grasping. We further distill the multi-step generator into a few-step student via distribution-matching distillation for efficient deployment. At the time of writing, \model{} achieves first place on Track~1 and second place on Track~2 of the WorldArena~2.0 Challenge. Our model and code will be publicly available.