  In general, hand pose estimation aims to improve the robustness of model
performance in the real-world scenes. However, it is difficult to enhance the
robustness since existing datasets are obtained in restricted environments to
annotate 3D information. Although neural networks quantitatively achieve a high
estimation accuracy, unsatisfied results can be observed in visual quality.
This discrepancy between quantitative results and their visual qualities
remains an open issue in the hand pose representation. To this end, we propose
a mesh represented recycle learning strategy for 3D hand pose and mesh
estimation which reinforces synthesized hand mesh representation in a training
phase. To be specific, a hand pose and mesh estimation model first predicts
parametric 3D hand annotations (i.e., 3D keypoint positions and vertices for
hand mesh) with real-world hand images in the training phase. Second, synthetic
hand images are generated with self-estimated hand mesh representations. After
that, the synthetic hand images are fed into the same model again. Thus, the
proposed learning strategy simultaneously improves quantitative results and
visual qualities by reinforcing synthetic mesh representation. To encourage
consistency between original model output and its recycled one, we propose
self-correlation loss which maximizes the accuracy and reliability of our
learning strategy. Consequently, the model effectively conducts self-refinement
on hand pose estimation by learning mesh representation from its own output. To
demonstrate the effectiveness of our learning strategy, we provide extensive
experiments on FreiHAND dataset. Notably, our learning strategy improves the
performance on hand pose and mesh estimation without any extra computational
burden during the inference.
