  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 NToP570K (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. A pretrained ViTPose-B
model achieves an improvement in AP of 33.3 % on our validation set for 2D HPE
after finetuning on our training set. A similarly finetuned HybrIK-Transformer
model gains 53.7 mm reduction in PA-MPJPE for 3D HPE on the validation set.
