The neural morph model. This generates a set of highly compressed morph targets to approximate a target deformation based on bone rotations and/or curve inputs. During inference, the neural network inside this model runs on the CPU and outputs the morph target weights for the morph targets it generated during training. Groups of bones and curves can also be defined. Those groups will generate a set of morph targets together as well. This can help when shapes depend on multiple inputs. An external morph target set is generated during training and serialized inside the ML Deformer asset that contains this model. When the ML Deformer component initializes the morph target set is registered. Most of the heavy lifting is done by the UMLDeformerMorphModel class. The neural morph model has two modes: local and global. See the ENeuralMorphMode for a description of the two.