Semantic Motion Correction Via Iterative Nonlinear Optimization and Animation
This addresses a domain-specific problem for animation creators, offering an incremental improvement in motion correction for sports scenarios.
The paper tackles the problem of correcting a goalkeeper's motion in 2D animation to ensure they block a penalty kick, using an iterative nonlinear optimization scheme to adjust the skeleton for successful deflection while maintaining semantic similarity to the original motion.
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.