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Effectively recognizing human actions from variant viewpoints is crucial for successful collaboration between humans and robots. Deep learning approaches have achieved promising performance in action recognition given sufficient well-annotated data from the real world. However, collecting and annotating real-world videos can be challenging, particularly for rare or violent actions. Synthetic data, on the other hand, can be easily obtained from simulators with fine-grained annotations and variant modalities. To learn domain-invariant feature representations, we propose a novel method to distill the pseudo labels from the strong mesh-based action recognition model into a light-weighted I3D model. In this way, the model can leverage robust 3D representations and maintain real-time inference speed. We empirically evaluate our model on the Mixamo→Kinetics dataset. The proposed model achieves state-of-the-art performance compared to the existing video domain adaptation methods.
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