NToP

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.