Motion Matching consists of acquiring data about movement phenomena in the real world and providing a virtual representation of it automatically. As opposed to individuals hand-crafting state-graphs, Motion Matching allows movements to be selected from a dataset using an algorithm. Motion capture suits are often used to map a jointed node-like structure of human movement for the dataset. Algorithms such as nearest neighbor can then be used to predict and provide the next closest frame data using velocity data across nodes in the previous time step. For example, a human slowing down, remaining stationary on their left foot, then proceeding to move their right foot forward is likely to turn to the left.

Additional information regarding Motion Matching using a nearest neighbor approach can be found in Santiago Moreno Diaz's conference paper "Motion Matching in the Unreal Engine" (2019). Additional information regarding improvements to Motion Matching algorithms can be found in Daniel Holden et. al., "Learned Motion Matching" ACM Trans. Graph. 39, 4, Article 1 (2020).
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