  Reconstructing interacting hands from a single RGB image is a very
challenging task. On the one hand, severe mutual occlusion and similar local
appearance between two hands confuse the extraction of visual features,
resulting in the misalignment of estimated hand meshes and the image. On the
other hand, there are complex spatial relationship between interacting hands,
which significantly increases the solution space of hand poses and increases
the difficulty of network learning. In this paper, we propose a decoupled
iterative refinement framework to achieve pixel-alignment hand reconstruction
while efficiently modeling the spatial relationship between hands.
Specifically, we define two feature spaces with different characteristics,
namely 2D visual feature space and 3D joint feature space. First, we obtain
joint-wise features from the visual feature map and utilize a graph convolution
network and a transformer to perform intra- and inter-hand information
interaction in the 3D joint feature space, respectively. Then, we project the
joint features with global information back into the 2D visual feature space in
an obfuscation-free manner and utilize the 2D convolution for pixel-wise
enhancement. By performing multiple alternate enhancements in the two feature
spaces, our method can achieve an accurate and robust reconstruction of
interacting hands. Our method outperforms all existing two-hand reconstruction
methods by a large margin on the InterHand2.6M dataset.
