  Visual segmentation has seen tremendous advancement recently with ready
solutions for a wide variety of scene types, including human hands and other
body parts. However, focus on segmentation of human hands while performing
complex tasks, such as manual assembly, is still severely lacking. Segmenting
hands from tools, work pieces, background and other body parts is extremely
difficult because of self-occlusions and intricate hand grips and poses. In
this paper we introduce BusyHands, a large open dataset of pixel-level
annotated images of hands performing 13 different tool-based assembly tasks,
from both real-world captures and virtual-world renderings. A total of 7906
samples are included in our first-in-kind dataset, with both RGB and depth
images as obtained from a Kinect V2 camera and Blender. We evaluate several
state-of-the-art semantic segmentation methods on our dataset as a proposed
performance benchmark.
