We present Scene2Demo, an automated framework for offline embodied data generation. Given a single RGB scene image and a user query, Scene2Demo constructs an interactive simulated scene and generates executable task configurations, primitive action flows, multi-view execution videos, and offline robot-learning datasets. Its five role-specialized generation modules perform task understanding, symbolic decomposition, BDDL configuration generation, object-size adjustment, and action-flow planning. The resulting object-centric task specifications and action transitions are organized as path search over a scene-conditioned object-action graph. Failed or incomplete executions are refined by two feedback agents: a Robotic Safety Inspector that produces step-wise visual critiques and a Robotic Task Supervisor that revises action sequences or parameters using the rollout and previous evolution history. Across 102 automatically generated primitive scene-task pairs, Scene2Demo achieves a 71.6% execution success rate; on four representative long-horizon tasks, feedback-driven refinement improves both task success and subtask-level execution quality over primitive-only execution. Comparisons with RoboGen and GenSim2 show stronger execution performance under automated data-generation settings. Finally, behavior cloning policies achieve 96.0% and 92.0% success on two representative tasks, showing that the generated data supports downstream policy learning. Our project page is available at https://scene2demo-anon.github.io/.

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