For Human Action Recognition tasks (HAR), 3D Convolutional Neural Networks have proven to be highly effective, achieving state-of-the-art results. This study introduces a novel streaming architecture-based toolflow for mapping such models onto FPGAs considering the model's inherent characteristics and the features of the targeted FPGA device. The HARFLOW3D toolflow takes as input a 3D CNN in ONNX format and a description of the FPGA characteristics, generating a design that minimises the latency of the computation. The toolflow is comprised of a number of parts, including (i) a 3D CNN parser, (ii) a performance and resource model, (iii) a scheduling algorithm for executing 3D models on the generated hardware, (iv) a resource-aware optimisation engine tailored for 3D models, (v) an automated mapping to synthesizable code for FPGAs. The ability of the toolflow to support a broad range of models and devices is shown through a number of experiments on various 3D CNN and FPGA system pairs. Furthermore, the toolflow has produced high-performing results for 3D CNN models that have not been mapped to FPGAs before, demonstrating the potential of FPGA-based systems in this space. Overall, HARFLOW3D has demonstrated its ability to deliver competitive latency compared to a range of state-of-the-art hand-tuned approaches, being able to achieve up to 5× better performance compared to some of the existing works. The tool is available at https://github.com/ptoupas/harflow3d.