# Sim2Real Transfer

DataFarm, from Peking University and NVIDIA, hands the teleoperator's job to a coding agent: given one calibration clip and a data request, the agent rebuilds the scene in simulation, writes an expert controller and a success judge, and a video model conditioned on real images renders multi-view video with action labels. On five real bimanual tasks, policies trained only on farm data reached 93 successes in 100 trials in the original scene, against 84 for an equal number of teleoperated episodes, and kept 87 under a tablecloth and coloured light absent from both training sets, where teleoperation-trained policies fell to 46.

DataFarm, from Peking University and NVIDIA, hands the teleoperator's job to a coding agent: given one calibration clip and a data request, the agent rebuilds the scene in simulation, writes an expert controller and a success judge, and a video model conditioned on real images renders multi-view video with action labels. On five real bimanual tasks, policies trained only on farm data reached 93 successes in 100 trials in the original scene, against 84 for an equal number of teleoperated episodes, and kept 87 under a tablecloth and coloured light absent from both training sets, where teleoperation-trained policies fell to 46.

Human micro-Doppler from millimeter-wave radar has lacked a scalable data source: forward-only simulators, learned generators that hide the scene in network weights, and static-scene inverse renderers with no slow-time axis all fail to fit a real capture of a moving body. 4DPS represents the scene as oriented points carrying material, normal and velocity, and splats each point into range and Doppler bins through a closed-form Doppler point spread function, emitting the raw complex return a real radar records. On HuPR and RT-Pose the fitted renders reach median micro-Doppler correlations of 0.988 and 0.966, and pose models trained on renders alone keep 0.59 and 0.76 of their real-trained accuracy on real data.

Human micro-Doppler from millimeter-wave radar has lacked a scalable data source: forward-only simulators, learned generators that hide the scene in network weights, and static-scene inverse renderers with no slow-time axis all fail to fit a real capture of a moving body. 4DPS represents the scene as oriented points carrying material, normal and velocity, and splats each point into range and Doppler bins through a closed-form Doppler point spread function, emitting the raw complex return a real radar records. On HuPR and RT-Pose the fitted renders reach median micro-Doppler correlations of 0.988 and 0.966, and pose models trained on renders alone keep 0.59 and 0.76 of their real-trained accuracy on real data.