MotionFlow: Joint Motion Priors and Appearance Enhancement for High-Accuracy Optical Flow Estimation

Zixu Wang, Congxuan Zhang, Zhen Chen, Hongye Chen, Liyue Ge, Ke Lu

Abstract: Although optical flow estimation has improved significantly in recent years, large displacements and occlusions remain challenging for current methods due to motion discontinuities that may hinder accurate feature correspondences in these regions, leading to degraded performance. To address this challenge, we propose a novel method named MotionFlow for high-accuracy optical flow estimation. In the encoding stage, we integrate multi-scale features enhance motion and context appearance information via cross- and inter-enhancement module. Subsequently, cross-frame features are utilized to establish motion priors, thereby providing essential prior knowledge for motion estimation. During the decoding stage, we align appearance features of the target frame with the reference frame through warping to retrieve missing context crucial for motion decoding. Experimental results demonstrate the efficacy of our approach, achieving state-of-the-art performance, particularly outperforming online benchmarks on Sintel Final pass and KITTI-2015 datasets.