As target tracking is arousing more and more interest, the necessity to reliably assess tracking algorithms in any conditions is becoming essential. The evaluation of such algorithms requires a database of sequences representative of the whole range of conditions in which the tracking system is likely to operate, together with its associated ground truth. However, building such a database with real sequences, and collecting the associated ground truth appears to be hardly possible and very time-consuming. Therefore, more and more often, synthetic sequences are generated by complex and heavy simulation platforms to evaluate the performance of tracking algorithms. Some methods have also been proposed using simple synthetic sequences generated without such complex simulation platforms. These sequences are generated from a finite number of discriminating parameters, and are statistically representative, as regards these parameters, of real sequences. They are very simple and not photorealistic. This paper shows how reliable non-photorealistic synthetic sequences are, and how the number of parameters can be increased to synthesize more elaborated scenes in order to deal with more complex target and background texture characteristics and relative motion, including 3D deformations and occlusions. These synthesized sequences are easily generated from any desired scene characteristics, and can be reliably used for tracking algorithms evaluation in any conditions.