This paper presents Dream2Act, a robot-centric framework enabling zero-shot interaction through generative video synthesis for humanoid robots. The approach addresses the morphology gap in traditional human-to-robot motion retargeting by synthesizing robot-native motion directly. Given a third-person image of the robot and target object, video generation models envision the robot completing tasks with morphology-consistent motion. A high-fidelity pose extraction system recovers physically feasible joint trajectories from synthesized videos, which are subsequently executed via a general-purpose whole-body controller.