  In this paper, we consider the challenging task of simultaneously locating
and recovering multiple hands from a single 2D image. Previous studies either
focus on single hand reconstruction or solve this problem in a multi-stage way.
Moreover, the conventional two-stage pipeline firstly detects hand areas, and
then estimates 3D hand pose from each cropped patch. To reduce the
computational redundancy in preprocessing and feature extraction, for the first
time, we propose a concise but efficient single-stage pipeline for multi-hand
reconstruction. Specifically, we design a multi-head auto-encoder structure,
where each head network shares the same feature map and outputs the hand
center, pose and texture, respectively. Besides, we adopt a weakly-supervised
scheme to alleviate the burden of expensive 3D real-world data annotations. To
this end, we propose a series of losses optimized by a stage-wise training
scheme, where a multi-hand dataset with 2D annotations is generated based on
the publicly available single hand datasets. In order to further improve the
accuracy of the weakly supervised model, we adopt several feature consistency
constraints in both single and multiple hand settings. Specifically, the
keypoints of each hand estimated from local features should be consistent with
the re-projected points predicted from global features. Extensive experiments
on public benchmarks including FreiHAND, HO3D, InterHand2.6M and RHD
demonstrate that our method outperforms the state-of-the-art model-based
methods in both weakly-supervised and fully-supervised manners. The code and
models are available at {https://github.com/zijinxuxu/SMHR}.
