MoML: Online Meta Adaptation for 3D Human Motion Prediction

Dong Wei

Abstract:

In the academic field, the research on human motion prediction tasks mainly focuses on exploiting the observed information to forecast human movements accurately in the
near future horizon. However, a significant gap appears
when it comes to the application field, as current models
are all trained offline, with fixed parameters that are inherently suboptimal to handle the complex yet ever-changing
nature of human behaviors. To bridge this gap, in this paper, we introduce the task of online meta adaptation for human motion prediction, based on the insight that finding
“smart weights” capable of swift adjustments to suit different motion contexts along the time is a key to improving
predictive accuracy. We propose MoML, which ingeniously
borrows the bilevel optimization spirit of model-agnostic
meta-learning, to transform previous predictive mistakes
into strong inductive biases to guide online adaptation. This
is achieved by our MoAdapter blocks that can learn error information by facilitating efficient adaptation via a few
gradient steps, which fine-tunes our meta-learned “smart”
initialization produced by the generic predictor. Considering real-time requirements in practice, we further propose
Fast-MoML, a more efficient variant of MoML that features
a closed-form solution instead of conventional gradient update. Experimental results show that our approach can effectively bring many existing offline motion prediction models online, and improves their predictive accuracy.