3D Human Pose Estimation Using Möbius Graph Convolutional Networks
This addresses the problem of inefficient and less accurate 3D human pose estimation for applications in human behavior analysis, representing an incremental improvement over existing GCN methods.
The paper tackled the limitation of graph convolutional networks in 3D human pose estimation by proposing MöbiusGCN, which explicitly encodes transformations between joints, resulting in a 90-98% parameter reduction and state-of-the-art performance on benchmarks like Human3.6M and MPI-INF-3DHP.
3D human pose estimation is fundamental to understanding human behavior. Recently, promising results have been achieved by graph convolutional networks (GCNs), which achieve state-of-the-art performance and provide rather light-weight architectures. However, a major limitation of GCNs is their inability to encode all the transformations between joints explicitly. To address this issue, we propose a novel spectral GCN using the Möbius transformation (MöbiusGCN). In particular, this allows us to directly and explicitly encode the transformation between joints, resulting in a significantly more compact representation. Compared to even the lightest architectures so far, our novel approach requires 90-98% fewer parameters, i.e. our lightest MöbiusGCN uses only 0.042M trainable parameters. Besides the drastic parameter reduction, explicitly encoding the transformation of joints also enables us to achieve state-of-the-art results. We evaluate our approach on the two challenging pose estimation benchmarks, Human3.6M and MPI-INF-3DHP, demonstrating both state-of-the-art results and the generalization capabilities of MöbiusGCN.