SimForce: Force and Surface Electromyography from Full Body Video with Graph Neural Nets

We propose a novel framework, named SimForce, for simultaneously estimating skeletal pose, ground reaction force and surface electromyography from an input video. Simforce predicts the more biomechanically accurate 3D human pose and shape of a given subject, along with their proposed muscle activations and resultant ground reaction force which leads to their input motion. Previous research has either focused on estimating these attributes singly and not treated them as a related task by taking into account the inherent shared motion between the three. In contrast, SimForce is designed to take advantage of the shared biological structure of the human body and its intrinsic connections to infer these attributes jointly using past, current, and future frames. SimForce features a newly introduced temporal and attention aware GCN-based architecture. To learn the subtle links between the body parts and how it affects the distribution of weight on the muscles over time, we introduce the Spatially Aware Attention Module.