STMPL: Human Soft-Tissue Simulation
This addresses the need for efficient soft-tissue simulation in interactive applications, representing an incremental improvement over existing methods.
The paper tackles the problem of slow and resource-intensive soft-tissue simulation in human bodies for applications like virtual reality and gaming by proposing a data-driven simulator that extends the SMPL model with a soft tissue layer and uses 2D UV maps and a UNET architecture, achieving high-accuracy inference in real time.
In various applications, such as virtual reality and gaming, simulating the deformation of soft tissues in the human body during interactions with external objects is essential. Traditionally, Finite Element Methods (FEM) have been employed for this purpose, but they tend to be slow and resource-intensive. In this paper, we propose a unified representation of human body shape and soft tissue with a data-driven simulator of non-rigid deformations. This approach enables rapid simulation of realistic interactions. Our method builds upon the SMPL model, which generates human body shapes considering rigid transformations. We extend SMPL by incorporating a soft tissue layer and an intuitive representation of external forces applied to the body during object interactions. Specifically, we mapped the 3D body shape and soft tissue and applied external forces to 2D UV maps. Leveraging a UNET architecture designed for 2D data, our approach achieves high-accuracy inference in real time. Our experiment shows that our method achieves plausible deformation of the soft tissue layer, even for unseen scenarios.