Unsupervised 3D Skeleton-Based Action Recognition using Cross-Attention with Conditioned Generation Capabilities

Human action recognition plays a pivotal role in various real-world applications, including surveillance systems, robotics, and occupant monitoring in the car interior. With such a diverse range of domains, the demand for generalization becomes increasingly crucial. In this work, we propose a cross-attention-based encoder-decoder approach for unsupervised 3D skeleton-based action recognition. Specifically, our model takes a skeleton sequence as input for the encoder and further applies masking and noise to the original sequence for the decoder. By training the model to reconstruct the original skeleton sequence, it simultaneously learns to capture the underlying patterns of actions. Extensive experiments on NTU and NW-UCLA datasets demonstrate the state-of-the-art performance as well as the impressive generalizability of our proposed approach. Moreover, our experiments reveal that our approach is capable of generating conditioned skeleton sequences, offering the potential to enhance small datasets or generate samples of under-represented classes in imbalanced datasets. Our code will be published on GitHub.