The field of computer vision is witnessing significant advancements in optical flow estimation and motion mapping, with a focus on improving accuracy, efficiency, and robustness in challenging scenarios. Researchers are exploring innovative approaches to address limitations in traditional methods, such as the use of diffusion models, implicit regularization, and neural implicit functions. These advancements have the potential to enhance various applications, including precision agriculture, robotics, and autonomous monitoring systems. Notable papers in this area include: E-MoFlow, which proposes an unsupervised framework for joint egomotion and optical flow estimation via implicit regularization. Removing Cost Volumes from Optical Flow Estimators, which introduces a training strategy to remove cost volumes from optical flow estimators, resulting in improved inference speed and reduced memory requirements.