  We propose a real-time DNN-based technique to segment hand and object of
interacting motions from depth inputs. Our model is called DenseAttentionSeg,
which contains a dense attention mechanism to fuse information in different
scales and improves the results quality with skip-connections. Besides, we
introduce a contour loss in model training, which helps to generate accurate
hand and object boundaries. Finally, we propose and release our InterSegHands
dataset, a fine-scale hand segmentation dataset containing about 52k depth maps
of hand-object interactions. Our experiments evaluate the effectiveness of our
techniques and datasets, and indicate that our method outperforms the current
state-of-the-art deep segmentation methods on interaction segmentation.
