  Graph convolutional network (GCN) has achieved great success in single hand
reconstruction task, while interacting two-hand reconstruction by GCN remains
unexplored. In this paper, we present Interacting Attention Graph Hand
(IntagHand), the first graph convolution based network that reconstructs two
interacting hands from a single RGB image. To solve occlusion and interaction
challenges of two-hand reconstruction, we introduce two novel attention based
modules in each upsampling step of the original GCN. The first module is the
pyramid image feature attention (PIFA) module, which utilizes multiresolution
features to implicitly obtain vertex-to-image alignment. The second module is
the cross hand attention (CHA) module that encodes the coherence of interacting
hands by building dense cross-attention between two hand vertices. As a result,
our model outperforms all existing two-hand reconstruction methods by a large
margin on InterHand2.6M benchmark. Moreover, ablation studies verify the
effectiveness of both PIFA and CHA modules for improving the reconstruction
accuracy. Results on in-the-wild images and live video streams further
demonstrate the generalization ability of our network. Our code is available at
https://github.com/Dw1010/IntagHand.
