This paper presents a comprehensive system for adaptive replay of a computer animation sequence featuring the locomotion of a synthetic animal. The kinematic locomotion gait, which is supposed to be mostly stationary, is tuned before being injected on the dynamic model of the animated animal. The animator can take benefit from this new flexibility in order to fulfil some goals, like steering the animal towards a target. The reference gait kinematic trajectories are expanded on a wavelet redundant bases, and tuned through varying the wavelet parameters. Because the optimization process is time-consuming, we propose to carry-out all optimizations beforehand and send the outcomes in a training set for a neural network, whose role is to compress data and to interpolate in between the training samples. At runtime, the network emulates the behaviour of the optimization process and automatically tailors the gait parameters. This approach paves the way towards the definition of a ready-to-use model for animated gaits.