Work

Video Frame Interpolation for Extreme Motion Scenes Based on Dual Alignment and Region-Adaptive Interaction

Year: 2025

Type: article

Abstract: Video frame interpolation in ultra-high-definition extreme motion scenes remains highly challenging due to large displacements, nonlinear motion, and occlusions that disrupt spatio-temporal symmetry. To address this issue, this study proposes a frame interpolation method for extreme motion scenes based on dual alignment and region-adaptive interaction from the perspectives of cross-frame localization and adaptive reconstruction. Specifically, we design a two-stage motion information alignment strategy that obtains two types of motion information via optical flow estimation and offset estimation, and it progressively guides reference pixels for accurate long-range cross-frame localization, mitigating structural misalignment caused by limited receptive fields while simultaneously alleviating spatiotemporal asymmetry caused by inconsistent inter-frame motion speed and direction. Based on this, we introduce a region-adaptive interaction module that automatically adapts motion representations for different regions through cross-frame interaction and leverages distinct attention pathways to accurately capture both the global context and local high-frequency motion details. This achieves a dynamic feature fusion tailored to regional characteristics, significantly enhancing the model’s ability to perceive the overall structure and texture details in extreme motion scenarios. In addition, the introduction of a motion compensation module explicitly captures pixel motion relationships by constructing a global correlation matrix that compensates for the positioning errors of the dual alignment module in extreme motion or occlusion areas. The experimental results demonstrate that the proposed method achieves excellent overall performance in ultra-high-definition extreme motion scenes, with a PSNR improvement of 0.05 dB over state-of-the-art methods. In multi-frame interpolation tasks, it achieves an average PSNR gain of 0.31 dB, demonstrating strong cross-scene interpolation capability.

Source: Symmetry

DOI: 10.3390/sym17122097

Authors: Xin Ning, Jiantao Qu, Junyi Duan, Kun Yang, Youdong Ding

Institutions: Shanghai University, PLA Information Engineering University, Taiyuan University of Technology

Language: English

Topic: Advanced Image Processing Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: Natural Science Foundation of Shanghai, National Natural Science Foundation of China