Remote photoplethysmography (rPPG) is an optical technique that measures physiological signals from facial videos by analyzing subtle changes in the skin blood volume. However, rPPG signals generated in practical applications are easily affected by external environmental factors and the state of individuals, leading to irregular waveform variations that increase the difficulty in heart rate estimation. To improve the regularity of generated rPPG signals, we propose a standardized rPPG signal generation method. Specifically, facial videos are fed into the generator of a generative adversarial network (GAN) to predict a rough rPPG signal by supervised learning. In addition, a mathematical signal synthesizer model is used to generate noise-free standardized rPPG signals, which are subsequently fed into a discriminator along with the predicted signal for adversarial learning. This enables the generator to learn more standardized waveforms. As a result, the predicted signal waveform by the generator becomes closer to the waveform distribution of real rPPG signals. The proposed method is validated on the widely used MAHNOB-HCI, UBFC-rPPG, and MMSE-HR databases and shows significant improvements in the prediction accuracy and signal-to-noise ratio.