  In this paper, 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 demonstrate
state-of-the-art results on NTU-60, NTU-120, Kinetics-skeleton and UWA3D
Multiview Activity II.
