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In this paper, we propose a Few-shot Learning pipeline for 3D skeleton-basedaction recognition by Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE).To factor out misalignment between query and support sequences of 3D bodyjoints, we propose an advanced variant of Dynamic Time Warping which jointlymodels each smooth path between the query and support frames to achievesimultaneously the best alignment in the temporal and simulated cameraviewpoint spaces for end-to-end learning under the limited few-shot trainingdata. Sequences are encoded with a temporal block encoder based on SimpleSpectral Graph Convolution, a lightweight linear Graph Neural Network backbone(we also include a setting with a transformer). Finally, we propose asimilarity-based loss which encourages the alignment of sequences of the sameclass while preventing the alignment of unrelated sequences. We demonstratestate-of-the-art results on NTU-60, NTU-120, Kinetics-skeleton and UWA3DMultiview Activity II.

3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Na\"ive