Here, we present an end-to-end method to create 2D animation for a goalkeeper
attempting to block a penalty kick, and then correct that motion using an
iterative nonlinear optimization scheme. The input is a raw video that is fed
into pose and object detection networks to find the skeleton of the goalkeeper
and the ball. The output is a set of key frames of the skeleton associated with
the corrected motion so that if the goalkeeper missed the ball, the animation
will show then successfully deflecting it. Our method is robust enough correct
different kinds of mistakes the goalkeeper can make, such as not lunging far
enough or jumping to the incorrect side. Our method is also meant to be
semantically similar to the goalkeeper's original motion, which helps keep our
animation grounded with respect to actual human behavior.

提出了一种用于为扑救射门的门将创建 2D 动画并使用迭代非线性优化方案纠正该动作的端到端方法，以便于保持动画的语义相似性和实际人类行为相关性。

通过迭代非线性优化和动画进行语义运动校正

Semantic Motion Correction Via Iterative Nonlinear Optimization and Animation

As an amateur goalkeeper playing grassroots soccer, who better to learn from
than top professional goalkeepers? In this paper, we harness computer vision
and machine learning models to appraise the save technique of professionals in
a way those at lower levels can learn from. We train an unsupervised machine
learning model using 3D body pose data extracted from broadcast footage to
learn professional goalkeeper technique. Then, an "expected saves" model is
developed, from which we can identify the optimal goalkeeper technique in
different match contexts.

使用计算机视觉和机器学习模型评估职业门将的保存技术，并通过训练无监督机器学习模型和开发 “预期保存” 模型来识别在不同比赛情况下的最佳门将技巧。