This paper addresses the challenge of transferring human motion data to humanoid robots by proposing Neural Motion Retargeting (NMR), a novel framework that transforms static geometric mapping into a dynamics-aware learned process. The approach uses Clustered-Expert Physics Refinement (CEPR) with VAE-based motion clustering to group heterogeneous movements into latent motifs, significantly reducing computational overhead for reinforcement learning experts that project and repair noisy human motion data. Through Hessian analysis, the authors demonstrate that traditional optimization-based retargeting is inherently non-convex and prone to local optima, leading to physical artifacts. By reformulating the problem as learning data distribution rather than optimizing solutions, the framework achieves smooth, physically plausible whole-body robot control.