ICIMG-Net: Inject Context Information to Motion Generation for Optical Flow Estimation
Wenbo Yin, Congxuan Zhang, Zhen Chen, Cheng Feng, Liyue Ge, Zige Wang · 2025
Although the overall performance of existing optical flow estimation methods has improved rapidly, motion discontinuities caused by large displacements and occlusions remain significant challenges for accurate optical flow estimation. To address this issue, we propose a novel Inject Context Information for Motion Generation Network (ICIMG-Net). Firstly, we design a Context Injection Module (CIM), which enhances the generation of correlation volumes by injecting context information. This process supplements the semantic details needed for accurate pixel matching, improving matching precision. Then, we construct a Dual-Injection-GRU (DIG), which facilitates dual interactions between semantic and motion information to address the motion discontinuities problem. Finally, we conduct a comprehensively evaluat of our ICIMG-Net against state-of-the-art methods on the MPI-Sintel and KITTI benchmarks, demonstrating that our method achieves competitive results, particularly in complex synthetic and real-world traffic scenarios.