Post-Data Augmentation to Improve Deep Pose Estimation of Extreme and Wild Motions
This work addresses the difficulty of applying pose estimation techniques for users in non-ML fields or with limited computational resources, though it appears incremental as it builds on existing pre-trained models.
The paper tackles the problem of low accuracy in deep pose estimation for extreme and wild motions, such as acrobatics or sports, by proposing a method that uses pre-trained models to improve accuracy without requiring users to perform training themselves.
Contributions of recent deep-neural-network (DNN) based techniques have been playing a significant role in human-computer interaction (HCI) and user interface (UI) domains. One of the commonly used DNNs is human pose estimation. This kind of technique is widely used for motion capturing of humans, and to generate or modify virtual avatars. However, in order to gain accuracy and to use such systems, large and precise datasets are required for the machine learning (ML) procedure. This can be especially difficult for extreme/wild motions such as acrobatic movements or motions in specific sports, which are difficult to estimate in typically provided training models. In addition, training may take a long duration, and will require a high-grade GPU for sufficient speed. To address these issues, we propose a method to improve the pose estimation accuracy for extreme/wild motions by using pre-trained models, i.e., without performing the training procedure by yourselves. We assume our method to encourage usage of these DNN techniques for users in application areas that are out of the ML field, and to help users without high-end computers to apply them for personal and end use cases.