An ongoing trend for sample libraries throughout the years is that they are always getting larger! Musicians demand better quality samples, more articulations, finer control, and overall more realism in their virtual instruments. The two prominent methods of synthesis for instruments are wavetable-based libraries and physically modeled ones. While the first option offers more attainable realism, the latter ultimately has more flexibility.

Sample libraries have become more extensive, including more and more articulations, usually accessible by keyswitches. New technology has emerged to improve realism, such as release samples and round-robin alternating samples. Physical modeling has become more elaborate, reproducing more detailed sound models and accounting for more aspects of physics. However, both sampled instruments and physically modeled instruments face the same dilemma: the performer itself must be modeled.

The first problem to face is what to do with limited MIDI data, from the sequencer or MIDI keyboard. MIDI is a very keyboard-oriented format, which makes it naturally limited in terms of controls. Basically, you can control the pitch of the note, velocity, as well as a few controllers (including the pitch wheel). However, you are limited as to how many controllers you can use simultaneously in a live performance.

Example: Iconic Bass: Jaco uses the sustain pedal to activate the slide mode, which changes any overlapping notes into a smooth glissando. The speed of the slide is based on the velocity of the overlapping note. The ability to slide from one note to another is a important characteristic of the fretless bass.
Due to the limitations of simultaneously accessible MIDI controllers, some automatic interpretation needs to be made by the sample library. For example, which frets and strings to use in a guitar library must be intelligently determined. Bowed instruments must be able to control bow pressure, speed, and direction. Although these controls can be assigned to MIDI controllers, the practicality of having all these elements instantly controllable must be questioned, as to not limit the use of sample libraries to sequencing but include live performance from a MIDI keyboard. While sequencing allows you to fine tune every element of the sample library dynamically, having too many controls to keep track of can be overwhelming and counterproductive.
Example: Strawberry Electric Guitar includes a layer-based mapping system which allows you to define exactly how the instrument behaves. It includes controls for how to translate MIDI data as well as letting you adjust the automatic interpretation.
Another thing to consider is the limitations of not only the instrument, but the performer as well. Most sample libraries account for pitch range limitations, but how many model hand movement speed and the maximum reach of the player's hand? These both are important considerations in most instruments.

Modeling player limitations is essential when interpreting MIDI to automatically control elements of the instrument's performance. That way the scripting engine for the sample library has a framework to anticipate and automate characteristics of the instrument beyond the instant control of the MIDI keyboard or MIDI sequencing. This can involve controlling something as obvious as articulations...
Example: CoreBass Cherry Slapped models the thumb and index finger position in order to accurately determine whether a note should be slapped or popped in true slap bass playing style.
...or details such as fret noises:
Example: CoreBass Pear tracks the hand position on the fingerboard, not only to correctly decide which strings to use when playing, but to model the finger position to reproduce the sliding noise created when switching positions. The scripted finger positions follow traditional upright bass playing technique.
Modeling the player in virtual instruments allows users who aren't familiar with how the particular instrument is played to create realistic tracks. It also overcomes the characteristic artificial sound that virtual instruments tend to have, by replicating essential humanistic elements that would otherwise be too difficult and time-consuming to sequence manually.

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