As AI models are increasingly integrated into applications involving human interaction, understanding the alignment between human perception and machine vision has become essential. One example is the estimation of visual motion (optical flow) in dynamic applications such as driving assistance. While there are numerous optical flow datasets and benchmarks with ground truth information, human-perceived flow in natural scenes remains underexplored. We introduce HuPerFlow—a benchmark for human-perceived flow, measured at 2,400 locations across ten optical flow datasets, with \~38,400 response vectors collected through online psychophysical experiments. Our data demonstrate that human-perceived flow aligns with ground truth in spatiotemporally smooth locations while also showing systematic errors influenced by various environmental properties. Additionally, we evaluated several optical flow algorithms against human-perceived flow, uncovering both similarities and unique aspects of human perception in complex natural scenes. HuPerFlow is the first large-scale human-perceived flow benchmark for alignment between computer vision models and human perception, as well as for scientific exploration of human motion perception in natural scenes. The HuPerFlow benchmark will be available online upon acceptance.