PixelNeRF is a neural rendering framework that has shown great success in synthesizing photo-realistic images of complex 3D scenes. Based on the original NeRF, one major addition by PixelNeRF is its ability to generalize its rendering to new and unseen scenes. In this project, we look into how transfer learning might be used to further enhance PixelNeRF’s generalization capabilities. By pre-training pixelNeRF on a different dataset, we attempt to enhance its per- formance on a given dataset. We present sceneries created using PixelNeRF with transfer learning and compare losses and qualitative results with paper’s PixelNeRF.