Deep Appearance Consistent Human Pose Transfer

The fidelity of a pose transfer system depends on its ability to generate realistic images of a person under novel poses while preserving the desired human attributes (like face, hairstyle, and clothes). However, the visual fidelity is often compromised as the existing methods fail to extract rich appearance and pose features since they propagate the pose and the appearance information through the same pathway. Also, the repeated downsampling in these pathways leads to the loss of finer details, thus producing blurry results. Further, these methods use vanilla convolution that treats all the pixels as important and fail to focus primarily on significant regions needed for the desired transformation. This work proposes an appearance-consistent human pose transfer framework that progressively transforms the person in the source image to the desired target pose using the information from three pathways: an image pathway, a pose pathway, and an appearance pathway. We propose the use of gated convolution to dynamically extract features relevant for generating the transformed image. The appearance pathway generates an appearance code to produce an image consistent in appearance with that of the source image. We establish the efficacy of the proposed framework through an extensive set of experiments on DeepFashion, Market-1501, and the Action Class dataset. We also generate coherent action sequences through a given set of desired poses from the action class dataset that contains humans in three actions: golf, yoga/workouts, and tennis.