  Part segmentations provide a rich and detailed part-level description of
objects. However, their annotation requires an enormous amount of work, which
makes it difficult to apply standard deep learning methods. In this paper, we
propose the idea of learning part segmentation through unsupervised domain
adaptation (UDA) from synthetic data. We first introduce UDA-Part, a
comprehensive part segmentation dataset for vehicles that can serve as an
adequate benchmark for UDA (https://qliu24.github.io/udapart). In UDA-Part, we
label parts on 3D CAD models which enables us to generate a large set of
annotated synthetic images. We also annotate parts on a number of real images
to provide a real test set. Secondly, to advance the adaptation of part models
trained from the synthetic data to the real images, we introduce a new UDA
algorithm that leverages the object's spatial structure to guide the adaptation
process. Our experimental results on two real test datasets confirm the
superiority of our approach over existing works, and demonstrate the promise of
learning part segmentation for general objects from synthetic data. We believe
our dataset provides a rich testbed to study UDA for part segmentation and will
help to significantly push forward research in this area.
