Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition

• URL: http://arxiv.org/abs/2302.13434v2
• Date: Tue, 25 Jul 2023 02:24:04 GMT
• Title: Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition
• Authors: Yifan Jiang, Han Chen, Hanseok Ko
• Abstract summary: We introduce a novel data augmentation method for skeleton-based action recognition tasks. Our method outperforms the state-of-the-art (SOTA) motion generation approaches on different naturality and diversity metrics.
• Score: 32.07659338674024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, skeleton-based human action has become a hot research topic because the compact representation of human skeletons brings new blood to this research domain. As a result, researchers began to notice the importance of using RGB or other sensors to analyze human action by extracting skeleton information. Leveraging the rapid development of deep learning (DL), a significant number of skeleton-based human action approaches have been presented with fine-designed DL structures recently. However, a well-trained DL model always demands high-quality and sufficient data, which is hard to obtain without costing high expenses and human labor. In this paper, we introduce a novel data augmentation method for skeleton-based action recognition tasks, which can effectively generate high-quality and diverse sequential actions. In order to obtain natural and realistic action sequences, we propose denoising diffusion probabilistic models (DDPMs) that can generate a series of synthetic action sequences, and their generation process is precisely guided by a spatial-temporal transformer (ST-Trans). Experimental results show that our method outperforms the state-of-the-art (SOTA) motion generation approaches on different naturality and diversity metrics. It proves that its high-quality synthetic data can also be effectively deployed to existing action recognition models with significant performance improvement.

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