Unsupervised optical flow estimation relies on the assumption that pixels characterizing the same observed object should exhibit a stable appearance across video frames. With this assumption, the long-standing principle behind flow estimation consists in optimizing a photometric loss that maximizes the similarity between paired pixels in successive frames. However, these frames could be subject to strong brightness changes due to the radiometric properties of scenes as well as their viewing conditions. In this paper, we present BrightFlow, a new method to train any optical flow estimation network in an unsupervised manner. It consists in training two networks that jointly estimate optical flow and brightness changes. These changes are then compensated in the photometric loss so that reconstruction errors due to shadows or reflections will not affect negatively the training. As this compensation mechanism is only used at training stage, our method does not impact the number of parameters or the complexity at inference. Extensive experiments conducted on standard datasets and optical flow architectures show a consistent gain of our method. Source code is available at https://github.com/CEA-LIST/BrightFlow.