The extraction of remote photoplethysmogram (PPG) signals from video with deep learning techniques for use in heart rate monitoring is a rapidly growing field, with dozens of end-to-end models being proposed. These models process the video in various ways to distill it to its most relevant information for cardiac signal extraction, but artifacts caused by subject motions and lighting conditions in the video remain a problem due to distortions in the waveform, making heart rate extraction difficult. In this paper, we propose a noise-aware post-processor network that takes generated position, head pose, and luminance information from the video as noise-correlating signals to be used to denoise a remote PPG signal generated by an existing base network. We show effective results on two separate datasets, PURE and MMPD, while using two different representative base models, DeepPhys and PhysNet, in conjunction with our post-processor, reducing mean absolute error by an average of 26% across all tests.