  Using videos to analyze pitchers in baseball can play a vital role in
strategizing and injury prevention. Computer vision-based pose analysis offers
a time-efficient and cost-effective approach. However, the use of accessible
broadcast videos, with a 30fps framerate, often results in partial body motion
blur during fast actions, limiting the performance of existing pose keypoint
estimation models. Previous works have primarily relied on fixed backgrounds,
assuming minimal motion differences between frames, or utilized multiview data
to address this problem. To this end, we propose a synthetic data augmentation
pipeline to enhance the model's capability to deal with the pitcher's blurry
actions. In addition, we leverage in-the-wild videos to make our model robust
under different real-world conditions and camera positions. By carefully
optimizing the augmentation parameters, we observed a notable reduction in the
loss by 54.2% and 36.2% on the test dataset for 2D and 3D pose estimation
respectively. By applying our approach to existing state-of-the-art pose
estimators, we demonstrate an average improvement of 29.2%. The findings
highlight the effectiveness of our method in mitigating the challenges posed by
motion blur, thereby enhancing the overall quality of pose estimation.
