Context-aware Human Motion Prediction

Understand

The problem of predicting human motion given a sequence of past observations is at the core of many applications in robotics and computer vision.

• Current state-of-the-art formulate this problem as a sequence-to-sequence task, in which a historical of 3D skeletons feeds a Recurrent Neural Network (RNN) that predicts future movements, typically in the order of 1 to 2 seconds.
• However, one aspect that has been obviated so far, is the fact that human motion is inherently driven by interactions with objects and/or other humans in the environment.
• In this paper, we explore this scenario using a novel context-aware motion prediction architecture.