论文标题
使用Möbius图卷积网络的3D人姿势估计
3D Human Pose Estimation Using Möbius Graph Convolutional Networks
论文作者
论文摘要
3D人类姿势估计对于理解人类行为至关重要。最近，图形卷积网络（GCN）实现了有希望的结果，该结果实现了最先进的性能并提供相当轻巧的体系结构。但是，GCN的主要局限性是它们无法明确编码关节之间的所有转换。为了解决这个问题，我们建议使用Möbius变换（MöbiusGCN）提出一种新型的光谱GCN。特别是，这使我们能够直接和显式地编码关节之间的转换，从而导致更紧凑的表示。与到目前为止最轻的体系结构相比，我们的新方法需要少90-98％的参数，即我们最轻的MöbiusGCN仅使用0.042m的可训练参数。除了减少急剧的参数外，明确编码关节的转换还使我们能够实现最新的结果。我们评估了两个具有挑战性的姿势估计基准Human36M和MPI-INF-3DHP的方法，证明了最新结果和Möbiusgcn的概括能力。
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.
