This paper presents Dream2Act, a robot-centric framework enabling zero-shot interaction for humanoid robots through generative video synthesis. By taking a third-person image of the robot and target object, the framework leverages video generation models to synthesize morphology-consistent motion for task completion. The approach employs a high-fidelity pose extraction system to recover physically feasible, robot-native joint trajectories from the synthesized videos. These trajectories are subsequently executed via a general-purpose whole-body controller, eliminating the need for extensive policy training or explicit motion retargeting that suffers from morphology gaps.