Human pose estimation is a fundamental task in computer vision with numerous applications in human-machine interaction, activity recognition, monitoring, surveillance, security, animation, and augmented reality. While existing pose estimation models perform well on humans in upright, unobstructed positions, humans are frequently in horizontal positions with occlusions like blankets in real-world scenarios. Much research has been done in using infrared, depth, and pressure images for in-bed pose estimation, but the comparatively inexpensive pose estimation with RGB images remains unexplored. In this paper, we address the task of accurately estimating in-bed human poses with severe occlusions induced by blankets using only RGB images. To overcome the under-representation of such poses and occlusions in conventional pose estimation datasets, a synthetic dataset is generated and used in the training process. We demonstrate the effectiveness of our method by fine-tuning a pre-trained pose estimation model with our synthetic dataset and evaluating the results on a real-world in-bed pose estimation dataset. We also integrate a domain adaptation method in the training process of the pose estimation model and evaluate the resulting increase in accuracy.