  Hand segmentation is a crucial task in first-person vision. Since
first-person images exhibit strong bias in appearance among different
environments, adapting a pre-trained segmentation model to a new domain is
required in hand segmentation. Here, we focus on appearance gaps for hand
regions and backgrounds separately. We propose (i) foreground-aware image
stylization and (ii) consensus pseudo-labeling for domain adaptation of hand
segmentation. We stylize source images independently for the foreground and
background using target images as style. To resolve the domain shift that the
stylization has not addressed, we apply careful pseudo-labeling by taking a
consensus between the models trained on the source and stylized source images.
We validated our method on domain adaptation of hand segmentation from real and
simulation images. Our method achieved state-of-the-art performance in both
settings. We also demonstrated promising results in challenging multi-target
domain adaptation and domain generalization settings. Code is available at
https://github.com/ut-vision/FgSty-CPL.
