HUMOS: Human Motion Model Conditioned on Body Shape
This addresses the limitation of uniform motion generation across different body types in computer vision and graphics, offering a more realistic and diverse solution.
The paper tackles the problem of generating realistic human motion that varies with body shape, introducing a model that uses unpaired data with cycle consistency and physics constraints to produce diverse, physically plausible motions, achieving better quantitative and qualitative results than state-of-the-art methods.
Generating realistic human motion is essential for many computer vision and graphics applications. The wide variety of human body shapes and sizes greatly impacts how people move. However, most existing motion models ignore these differences, relying on a standardized, average body. This leads to uniform motion across different body types, where movements don't match their physical characteristics, limiting diversity. To solve this, we introduce a new approach to develop a generative motion model based on body shape. We show that it's possible to train this model using unpaired data by applying cycle consistency, intuitive physics, and stability constraints, which capture the relationship between identity and movement. The resulting model generates diverse, physically plausible, and dynamically stable human motions that are both quantitatively and qualitatively more realistic than current state-of-the-art methods. More details are available on our project page https://CarstenEpic.github.io/humos/.