  We propose an automatic method for generating high-quality annotations for
depth-based hand segmentation, and introduce a large-scale hand segmentation
dataset. Existing datasets are typically limited to a single hand. By
exploiting the visual cues given by an RGBD sensor and a pair of colored
gloves, we automatically generate dense annotations for two hand segmentation.
This lowers the cost/complexity of creating high quality datasets, and makes it
easy to expand the dataset in the future. We further show that existing
datasets, even with data augmentation, are not sufficient to train a hand
segmentation algorithm that can distinguish two hands. Source and datasets will
be made publicly available.
