Leveraging Language Models to Optimize 3D Human Pose Estimation with Accurate Physical Contact Constraints

Our method leverages the semantic priors of large pretrained language models to convert natural language descriptions of physical interactions into mathematical constraints, which can then be optimized to refine 3D pose estimates and accurately capture self-contact and person-to-person contact.