论文标题
ATCA：基于弧形轨迹的模型，对视频框架插值的曲率关注
ATCA: an Arc Trajectory Based Model with Curvature Attention for Video Frame Interpolation
论文作者
论文摘要
视频框架插值是一项经典且具有挑战性的低级计算机视觉任务。最近，基于深度学习的方法取得了令人印象深刻的结果，并且已经证明，基于光流的方法可以合成质量更高的帧。但是，大多数基于流动的方法都假设两个输入帧之间具有恒定速度的线轨迹。只需少量工作就可以使用曲线轨迹进行预测，但这需要两个以上的帧作为输入来估计加速度，这需要更多的时间和内存才能执行。为了解决这个问题，我们提出了一个基于弧形轨迹的模型（ATCA），该模型仅从两个连续的框架中就学先学习运动，而且轻量级。实验表明，我们的方法的性能要比许多参数较少且推理速度更快的SOTA方法更好。
Video frame interpolation is a classic and challenging low-level computer vision task. Recently, deep learning based methods have achieved impressive results, and it has been proven that optical flow based methods can synthesize frames with higher quality. However, most flow-based methods assume a line trajectory with a constant velocity between two input frames. Only a little work enforces predictions with curvilinear trajectory, but this requires more than two frames as input to estimate the acceleration, which takes more time and memory to execute. To address this problem, we propose an arc trajectory based model (ATCA), which learns motion prior from only two consecutive frames and also is lightweight. Experiments show that our approach performs better than many SOTA methods with fewer parameters and faster inference speed.
