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.

In this paper, we focus on the problem of category-level object pose estimation, which is challenging due to the large intra-category shape variation. 3D graph convolution (3D-GC) based methods have been widely used to extract local geometric features, but they have limitations for complex shaped objects and are sensitive to noise. Moreover, the scale and translation invariant properties of 3D-GC restrict the perception of an object's size and translation information. In this paper, we propose a simple network structure, the HS-layer, which extends 3D-GC to extract hybrid scope latent features from point cloud data for category-level object pose estimation tasks. The proposed HS-layer: 1) is able to perceive local-global geometric structure and global information, 2) is robust to noise, and 3) can encode size and translation information. Our experiments show that the simple replacement of the 3D-GC layer with the proposed HS-layer on the baseline method (GPV-Pose) achieves a significant improvement, with the performance increased by 14.5% on 5°2cm metric and 10.3% on IoU75. Our method outperforms the state-of-the-art methods by a large margin (8.3% on 5°2cm, 6.9% on IoU75) on REAL275 dataset and runs in real-time (50 FPS)11Codeisavailable: https://github.com/Lynne-Zheng-Linfang/HS-Pose.