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Explicit Depth-Aware Blurry Video Frame Interpolation Guided by Differential Curves

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 variations that occur during fast object motion, leading to changes in object size and hindering interpolation performance. This paper proposes the Differential Curves-guided Blurry Video Frame Interpolation (DC-BVFI) framework, which leverages the differential curves theory to analyze and mitigate the effects of depth variations caused by object motion. Specifically, DC-BVFI consists of UBNet and MPNet. Unlike prior approaches that rely on optical flow for frame interpolation, MPNet is designed to estimate the 3D scene flow, which facilitates a more precise awareness of depth and velocity variations. Since scene flow cannot be directly inferred in the 2D frame space, UBNet is introduced to transform them into 3D point maps. Extensive experiments demonstrate that the proposed DC-BVFI framework surpasses state-of-the-art performance in simulated and real-world datasets.

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