Weakly-supervised hand part segmentation from depth images

Existing learning-based methods require a large number of labeled data to produce accurate part segmentation labels. However, acquiring ground truth labels is costly, giving rise to a need for methods that either require fewer labels or can utilize other currently available labels as a form of weak supervision for training. In this paper, in order to mitigate the burden of labeled-data acquisition, we propose a data-driven method for hand part segmentation on depth maps without any need for extra effort to obtain segmentation labels. The proposed method uses the labels already provided by public datasets in terms of major 3D hand joint locations to learn to estimate the hand shape and pose given a depth map. Given the pose and shape of a hand, the corresponding 3D hand mesh is generated using a deformable hand model and then rendered to a color image using a texture based on Linear Blend Skinning (LBS) weights of the hand model. The segmentation labels are then computed from the rendered color image. Since segmentation labels are not provided with current public datasets, we manually annotate a subset of the NYU dataset to perform quantitative evaluation of our method and show that a mIoU of 42% can be achieved with a model trained without using segmentation-based labels. Both qualitative and quantitative results confirm the effectiveness of our method. The code is publicly available for research purposes at: https://git.io/JmCBS.