  Predicting high-fidelity future human poses, from a historically observed
sequence, is decisive for intelligent robots to interact with humans. Deep
end-to-end learning approaches, which typically train a generic pre-trained
model on external datasets and then directly apply it to all test samples,
emerge as the dominant solution to solve this issue. Despite encouraging
progress, they remain non-optimal, as the unique properties (e.g., motion
style, rhythm) of a specific sequence cannot be adapted. More generally, at
test-time, once encountering unseen motion categories (out-of-distribution),
the predicted poses tend to be unreliable. Motivated by this observation, we
propose a novel test-time adaptation framework that leverages two
self-supervised auxiliary tasks to help the primary forecasting network adapt
to the test sequence. In the testing phase, our model can adjust the model
parameters by several gradient updates to improve the generation quality.
However, due to catastrophic forgetting, both auxiliary tasks typically tend to
the low ability to automatically present the desired positive incentives for
the final prediction performance. For this reason, we also propose a
meta-auxiliary learning scheme for better adaptation. In terms of general
setup, our approach obtains higher accuracy, and under two new experimental
designs for out-of-distribution data (unseen subjects and categories), achieves
significant improvements.
