Video Frame Interpolation (VFI) involves generating intermediate frames by leveraging both preceding and succeeding frames to enhance video quality. Traditional methods, particularly those based on U-Net architectures, often suffer from high computational complexity and memory demands due to their large parameter counts. In this paper, we introduce the Square Funnel Network (SF-Net), a novel architecture that significantly reduces the number of parameters while maintaining competitive performance. SF-Net employs a unique design that increases the third dimension of the input frame in deeper layers instead of increasing the number of filters, resulting in a simpler and more efficient model. Our architecture utilizes no more than 64 filters in nearly all layers, with only the final two layers using 128 filters each. Through both objective and subjective evaluations, SF-Net demonstrates high visual quality and efficiency, making it suitable for applications with limited computational resources. This work presents a promising direction for VFI, emphasizing parameter reduction without sacrificing performance.