Blurry video frame interpolation (BVFI), which aims to generate high-frame-rate clear videos from low-frame-rate blurry inputs, is a challenging yet significant task in computer vision. Current state-of-the-art approaches typically rely on linear or quadratic models to estimate intermediate motion. However, these methods often overlook depth-related changes, such as object size and viewing angle variations, which occur as objects move through the scene. This paper proposes a novel approach to addressing this challenge by leveraging differential curves that can describe motion velocity and depth changes.Specifically, we introduce an explicit framework, termed Differential Curve-guided Blurry Video Multi-Frame Interpolation (DC-BMFI), to eliminate the effects of depth variation caused by object motion on BVFI task. In contrast to existing methods that utilize optical flow for 2D awareness, we introduce an MPNet submodule within the DC-BMFI framework to estimate 3D scene flow, thereby enhancing the awareness of depth and velocity changes.To estimate the 3D scene flow from video frames, we propose a submodule termed UBNet to transform video frames into 3D camera space point maps and then estimate scene flow between point maps.Extensive experiments demonstrate that the proposed DC-BMFI surpasses state-of-the-art performance in simulated and real-world datasets.