Recent progress in robot learning has been driven by large-scale datasets and powerful visuomotor policy architectures, yet policy robustness remains limited by the substantial cost of collecting diverse demonstrations, particularly for spatial generalization in manipulation tasks. To reduce repetitive data collection, we present Real2Edit2Real, a framework that generates new demonstrations by bridging 3D editability with 2D visual data through a 3D control interface. Our approach first reconstructs scene geometry from multi-view RGB observations with a metric-scale 3D reconstruction model. Based on the reconstructed geometry, we perform depth-reliable 3D editing on point clouds to generate new manipulation trajectories while geometrically correcting the robot poses to recover physically consistent depth, which serves as a reliable condition for synthesizing new demonstrations. Finally, we propose a multi-conditional video generation model guided by depth as the primary control signal, together with action, edge, and ray maps, to synthesize spatially augmented multi-view manipulation videos. Experiments on four real-world manipulation tasks demonstrate that policies trained on data generated from only 1-5 source demonstrations can match or outperform those trained on 50 real-world demonstrations, improving data efficiency by up to 10-50x. Moreover, experimental results on height and texture editing demonstrate the framework's flexibility and extensibility, indicating its potential to serve as a unified data generation framework. Project website is https://real2edit2real.github.io/.

翻译：近年来，机器人学习的进展得益于大规模数据集和强大的视觉运动策略架构，然而策略的鲁棒性仍受到收集多样化演示（尤其是操作任务中的空间泛化）高昂成本的限制。为减少重复性数据收集，我们提出Real2Edit2Real框架，通过3D控制界面将3D可编辑性与2D视觉数据相结合，生成新的演示。该方法首先利用公制尺度的3D重建模型，从多视角RGB观测中重建场景几何结构。基于重建的几何结构，我们对点云进行深度可靠的3D编辑以生成新的操作轨迹，同时通过几何校正机械臂位姿恢复物理一致的深度信息，为合成新演示提供可靠条件。最后，我们提出一种以深度作为主控制信号的多条件视频生成模型（结合动作、边缘与射线图），合成空间增强的多视角操作视频。在四个真实世界操作任务上的实验表明，仅使用1-5个源演示生成的数据训练的策略，即可匹配甚至超越使用50个真实演示训练的策略，数据效率提升达10-50倍。此外，针对高度与纹理编辑的实验结果验证了该框架的灵活性与可扩展性，表明其具备成为统一数据生成框架的潜力。项目网站：https://real2edit2real.github.io/。