  With increasing applications of 3D hand pose estimation in various
human-computer interaction applications, convolution neural networks (CNNs)
based estimation models have been actively explored. However, the existing
models require complex architectures or redundant computational resources to
trade with the acceptable accuracy. To tackle this limitation, this paper
proposes HandFoldingNet, an accurate and efficient hand pose estimator that
regresses the hand joint locations from the normalized 3D hand point cloud
input. The proposed model utilizes a folding-based decoder that folds a given
2D hand skeleton into the corresponding joint coordinates. For higher
estimation accuracy, folding is guided by multi-scale features, which include
both global and joint-wise local features. Experimental results show that the
proposed model outperforms the existing methods on three hand pose benchmark
datasets with the lowest model parameter requirement. Code is available at
https://github.com/cwc1260/HandFold.
