This study focuses on generating fundamental frequency (F0) curves of singing voice from musical scores stored in a midi-like notation. Current statistical parametric approaches to singing F0 modeling meet difficulties in reproducing vibratos and the temporal details at note boundaries due to the oversmoothing tendency of statistical models. This paper presents a neural network based solution that models a pair of neighboring notes at a time (the transition model) and uses a separate network for generating vibratos (the sustain model). Predictions from the two models are combined by summation after proper enveloping to enforce continuity. In the training phase, mild misalignment between the scores and the target F0 is addressed by back-propagating the gradients to the networks' inputs. Subjective listening tests on the NITech singing database show that transition-sustain models are able to generate F0 trajectories close to the original performance.