Abstract: We present \textit{VoxelKP}, a novel fully sparse network architecture tailored for human keypoint estimation in LiDAR data.The key challenge is that objects are distributed sparsely in 3D space, while human keypoint detection requires detailed local information wherever humans are present.First, we introduce a dual-branch \textit{fully sparse spatial-context block} where the spatial branch focuses on learning the local spatial correlations between keypoints within each human instance, while the context branch aims to retain the global spatial information. Second, we use a \textit{spatially aware multi-scale BEV fusion} technique to leverage absolute 3D coordinates when projecting 3D voxels to a 2D grid encoding a bird's eye view for better preservation of the global context of each human instance.We evaluate our method on the Waymo dataset and achieve an improvement of $27\%$ on the MPJPE metric compared to the state-of-the-art, \textit{HUM3DIL}, trained on the same data, and $12\%$ against the state-of-the-art, \textit{GC-KPL}, pretrained on a $25\times$ larger dataset.To the best of our knowledge, \textit{VoxelKP} is the first single-staged, fully sparse network that is specifically designed for addressing the challenging task of 3D keypoint estimation from LiDAR data, achieving state-of-the-art performance. Our code is available at\url{https://}.