  We propose a Few-shot Learning pipeline for 3D skeleton-based action
recognition by Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE). To
factor out misalignment between query and support sequences of 3D body joints,
we propose an advanced variant of Dynamic Time Warping which jointly models
each smooth path between the query and support frames to achieve simultaneously
the best alignment in the temporal and simulated camera viewpoint spaces for
end-to-end learning under the limited few-shot training data. Sequences are
encoded with a temporal block encoder based on Simple Spectral Graph
Convolution, a lightweight linear Graph Neural Network backbone. We also
include a setting with a transformer. Finally, we propose a similarity-based
loss which encourages the alignment of sequences of the same class while
preventing the alignment of unrelated sequences. We show state-of-the-art
results on NTU-60, NTU-120, Kinetics-skeleton and UWA3D Multiview Activity II.
