  We present, for the first time, 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
first 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.
