  We present a comprehensive framework for egocentric interaction recognition
using markerless 3D annotations of two hands manipulating objects. To this end,
we propose a method to create a unified dataset for egocentric 3D interaction
recognition. Our method produces annotations of the 3D pose of two hands and
the 6D pose of the manipulated objects, along with their interaction labels for
each frame. Our dataset, called H2O (2 Hands and Objects), provides
synchronized multi-view RGB-D images, interaction labels, object classes,
ground-truth 3D poses for left & right hands, 6D object poses, ground-truth
camera poses, object meshes and scene point clouds. To the best of our
knowledge, this is the first benchmark that enables the study of first-person
actions with the use of the pose of both left and right hands manipulating
objects and presents an unprecedented level of detail for egocentric 3D
interaction recognition. We further propose the method to predict interaction
classes by estimating the 3D pose of two hands and the 6D pose of the
manipulated objects, jointly from RGB images. Our method models both inter- and
intra-dependencies between both hands and objects by learning the topology of a
graph convolutional network that predicts interactions. We show that our method
facilitated by this dataset establishes a strong baseline for joint hand-object
pose estimation and achieves state-of-the-art accuracy for first person
interaction recognition.
