Shape Preservation in Image Style Transfer for Gaze Estimation

Daiki Mushiake, Kentaro Otomo, Chihiro Nakatani, Norimichi Ukita

Abstract: This paper proposes image style transfer with shape preservation for gaze estimation. While several shape preservation constraints are proposed, we present additional shape preservation constraints using (i) dense pixelwise correspondences between the original and its transferred images and (ii) task-driven learning using gaze estimation error for directly improving gaze direction estimation. A variety of experiments with other SOTA methods, publicly-available datasets, and ablation studies validate the effectiveness of our method.