  In this paper, we propose to estimate 3D hand pose by recovering the 3D
coordinates of joints in a group-wise manner, where less-related joints are
automatically categorized into different groups and exhibit different features.
This is different from the previous methods where all the joints are considered
holistically and share the same feature. The benefits of our method are
illustrated by the principle of multi-task learning (MTL), i.e., by separating
less-related joints into different groups (as different tasks), our method
learns different features for each of them, therefore efficiently avoids the
negative transfer (among less related tasks/groups of joints). The key of our
method is a novel binary selector that automatically selects related joints
into the same group. We implement such a selector with binary values
stochastically sampled from a Concrete distribution, which is constructed using
Gumbel softmax on trainable parameters. This enables us to preserve the
differentiable property of the whole network. We further exploit features from
those less-related groups by carrying out an additional feature fusing scheme
among them, to learn more discriminative features. This is realized by
implementing multiple 1x1 convolutions on the concatenated features, where each
joint group contains a unique 1x1 convolution for feature fusion. The detailed
ablation analysis and the extensive experiments on several benchmark datasets
demonstrate the promising performance of the proposed method over the
state-of-the-art (SOTA) methods. Besides, our method achieves top-1 among all
the methods that do not exploit the dense 3D shape labels on the most recently
released FreiHAND competition at the submission date. The source code and
models are available at https://github.com/ moranli-aca/LearnableGroups-Hand.
