Human pose estimation (HPE) in the top-view using fisheye cameras presents a promising and innovative application domain. However, the availability of datasets capturing this viewpoint is extremely limited, especially those with high-quality 2D and 3D keypoint annotations. Addressing this gap, we leverage the capabilities of Neural Radiance Fields (NeRF) technique to establish a comprehensive pipeline for generating human pose datasets from existing 2D and 3D datasets, specifically tailored for the top-view fisheye perspective. Through this pipeline, we create a novel dataset NToP (NeRF-powered Top-view human Pose dataset for fisheye cameras) with over 570 thousand images, and conduct an extensive evaluation of its efficacy in enhancing neural networks for 2D and 3D top-view human pose estimation. Extensive evaluations on existing top-view 2D and 3D HPE datasets as well as our new real-world top-view 2D HPE dataset OmniLab prove that our dataset is effective and exceeds previous datasets in this field of research. The code and the trained models of NToP are available at https://github.com/yu-jingrui/NToP .