  Human motion forecasting, with the goal of estimating future human behavior
over a period of time, is a fundamental task in many real-world applications.
However, existing works typically concentrate on predicting the major joints of
the human body without considering the delicate movements of the human hands.
In practical applications, hand gesture plays an important role in human
communication with the real world, and expresses the primary intention of human
beings. In this work, we are the first to formulate a whole-body human pose
forecasting task, which jointly predicts the future body and hand activities.
Correspondingly, we propose a novel Encoding-Alignment-Interaction (EAI)
framework that aims to predict both coarse (body joints) and fine-grained
(gestures) activities collaboratively, enabling expressive and
cross-facilitated forecasting of 3D whole-body human motions. Specifically, our
model involves two key constituents: cross-context alignment (XCA) and
cross-context interaction (XCI). Considering the heterogeneous information
within the whole-body, XCA aims to align the latent features of various human
components, while XCI focuses on effectively capturing the context interaction
among the human components. We conduct extensive experiments on a
newly-introduced large-scale benchmark and achieve state-of-the-art
performance. The code is public for research purposes at
https://github.com/Dingpx/EAI.
