Recovering world-coordinate human motion from monocular videos with humanoid robot retargeting is significant for embodied intelligence and robotics. To avoid complex SLAM pipelines or heavy temporal models, we propose a lightweight, engineering-oriented framework that leverages SAM 3D Body (3DB) as a frozen perception backbone and uses the Momentum HumanRig (MHR) representation as a robot-friendly intermediate. Our method (i) locks the identity and skeleton-scale parameters of per tracked subject to enforce temporally consistent bone lengths, (ii) smooths per-frame predictions via efficient sliding-window optimization in the low-dimensional MHR latent space, and (iii) recovers physically plausible global root trajectories with a differentiable soft foot-ground contact model and contact-aware global optimization. Finally, we retarget the reconstructed motion to the Unitree G1 humanoid using a kinematics-aware two-stage inverse kinematics pipeline. Results on real monocular videos show that our method has stable world trajectories and reliable robot retargeting, indicating that structured human representations with lightweight physical constraints can yield robot-ready motion from monocular input.