Ms. Xiaoyu Zhu Profile

Xiaoyu Zhu

at Carnegie Mellon Univ

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Proceedings Article | 14 June 2023 Poster + Paper

Leveraging body pose estimation for gesture recognition in human-robot interaction using synthetic data

Xiaoyu Zhu, Celso de Melo, Alexander Hauptmann

Proceedings Volume 12529, 125290Z (2023) https://doi.org/10.1117/12.2664030

KEYWORDS: Data modeling, 3D modeling, Action recognition, Pose estimation, Gesture recognition

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Effectively recognizing human gestures from variant viewpoints plays a fundamental role in the successful collaboration between humans and robots. Deep learning approaches have achieved promising performance in gesture recognition. However, they are usually data-hungry and require large-scale labeled data, which are not usually accessible in a practical setting. Synthetic data, on the other hand, can be easily obtained from simulators with fine-grained annotations and variant modalities. Existing state-of-the-art approaches have shown promising results using synthetic data, but there is still a large performance gap between the models trained on synthetic data and real data. To learn domain-invariant feature representations, we propose a novel approach which jointly takes RGB videos and 3D meshes as inputs to perform robust action recognition. We empirically validate our model on the RoCoG-v2 dataset, which consists of a variety of real and synthetic videos of gestures from the ground and air perspectives. We show that our model trained on synthetic data can outperform state-of-the-art models under the same training setting and models trained on real data.

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