Can Text-to-3D Human Motion Models Really Understand Physical Actions?

Text-to-3D human motion generation aims to translate natural-language intentions into 3D human motions, serving as an important bridge between language understanding and human behavior generation, with broad potential for digital humans, computer animation, and human–computer interaction. Existing text-to-3D human motion generation methods primarily rely on large-scale motion data to learn motion representations and generative distributions, while improving generation quality and motion naturalness through generative modeling, motion priors, or post-training optimization. However, these approaches fail to establish effective supervision for the underlying physical laws and latent physical distributions of human motion during training. As a result, the generated trajectories can still deviate substantially from the physical distribution governing real human motion. To address this problem, we propose MoPhys (Motion Physics Modeling), a time-frequency modeling and physics-expert collaborative framework for physically consistent text-to-3D human motion generation. Specifically, MoPhys employs multi-scale time-frequency interactions to capture rich motion variations across different temporal scales, and introduces physics-inspired experts constructed from body observables to adaptively refine generated motions. MoPhys further transforms the structural and temporal information in real motions into direct supervision for the generative model, guiding it to learn the physical constraints embedded in the training data and thereby producing motions that better conform to the physical distribution of real human motion. Extensive experiments demonstrate that MoPhys consistently improves the physical plausibility of generated motions across diverse data distributions and multiple human motion generation paradigms, validating both the effectiveness and generalizability of the proposed framework.