Optical flow estimation is one of the fundamental tasks in low-level computer
vision, which describes the pixel-wise displacement and can be used in many
other tasks. From the apparent aspect, the optical flow can be viewed as the
correlation between the pixels in consecutive frames, so continuously refining
the correlation volume can achieve an outstanding performance. However, it will
make the method have a catastrophic computational complexity. Not only that,
the error caused by the occlusion regions of the successive frames will be
amplified through the inaccurate warp operation. These challenges can not be
solved only from the apparent view, so this paper rethinks the optical flow
estimation from the kinetics viewpoint.We propose a method combining the
apparent and kinetics information from this motivation. The proposed method
directly predicts the optical flow from the feature extracted from images
instead of building the correlation volume, which will improve the efficiency
of the whole network. Meanwhile, the proposed method involves a new
differentiable warp operation that simultaneously considers the warping and
occlusion. Moreover, the proposed method blends the kinetics feature with the
apparent feature through the novel self-supervised loss function. Furthermore,
comprehensive experiments and ablation studies prove that the proposed novel
insight into how to predict the optical flow can achieve the better performance
of the state-of-the-art methods, and in some metrics, the proposed method
outperforms the correlation-based method, especially in situations containing
occlusion and fast moving. The code will be public.

本文提出了一种新的方法，通过从图像提取特征直接预测光流，同时考虑到光流估计的运动学视角和外观视角，采用可微分的变形操作解决了由于运动不准确造成的误差及遮挡问题，并通过自监督损失函数将运动特征与外观特征相结合，实验证明该方法在光流预测上表现优于现有方法，尤其在包含遮挡和快速运动的情况下效果更好。