Fitness Aware Human Motion Generation with Fine-Tuning

Abstract

Diffusion models have recently gained considerable attention in 3D human motion generation due to their ability to handle complex human movements. However, existing models fail to incorporate the nuances presented by individual physical fitness levels. Therefore, we address this gap by integrating Functional Movement Screen (FMS) scores into diffusion models through fine-tuning, enabling the generation of fitness-aware motions. This approach transforms FMS data into HumanML3D format, optimises a base diffusion model, and introduces conditioning based on FMS scores. As a result, our fine-tuned model is capable of generating motions tailored to individual fitness levels and shows significant improvements in motion generation fidelity. Producing synthetic human motions conditioned on fitness levels is a novel approach that can be highly beneficial for various fields such as healthcare, sports, and entertainment.