Precision livestock farming uses artificial intelligence to individually monitor livestock activity and health. Tracking individuals over time can reveal health indicators that correlate with productivity and longevity.  For instance, locomotion patterns observed in lame pigs have been shown to correlate with poor animal welfare and productivity.  Kinematic analysis of pigs using pose estimates provides a means of assessing locomotion. New dense depth sensors have potential to achieve full 3D pose estimation and tracking.  However, the lack of annotated dense depth datasets has limited use of these sensors in detecting animal pose.  Current annotation methods rely on human labeling, but identifying hip and shoulder locations is difficult for pigs with few prominent features, and is especially difficult in depth images as these lack albedo texture.   This work proposes a solution to quickly generate high accuracy pig landmark annotations for depth-based pose estimation. We propose Depth-Infrared Annotation Transfer (DIAT), an approach that semi-automatically finds, identifies, and tracks marks visible in infrared, and transfers these labels to depth images. As a result, we are able to train a precise pig pose detector that operates on depth images.