Analyzing a player's technique in table tennis requires knowledge of the ball's 3D trajectory and spin. While, the spin is not directly observable in standard broadcasting videos, we show that it can be inferred from the ball's trajectory in the video. We present a novel method to infer the initial spin and 3D trajectory from the corresponding 2D trajectory in a video. Without ground truth labels for broadcast videos, we train a neural network solely on synthetic data. Due to the choice of our input data representation, physically correct synthetic training data, and using targeted augmentations, the network naturally generalizes to real data. Notably, these simple techniques are sufficient to achieve generalization. No real data at all is required for training. To the best of our knowledge, we are the first to present a method for spin and trajectory prediction in simple monocular broadcast videos, achieving an accuracy of 92.0% in spin classification and a 2D reprojection error of 0.19% of the image diagonal.

本研究解决了在乒乓球广播视频中难以直接观察球的旋转问题，通过2D轨迹推断3D轨迹和初始转动。我们提出了一种新颖的方法，依靠合成数据训练神经网络，利用物理正确的合成训练数据和有针对性的增强，实现在真实数据中的有效泛化，达到92.0%的旋转分类准确率和0.19%的2D重投影误差。

乒乓球转动和轨迹分析的广播视频研究：基于物理的合成到真实转移