  Human densepose estimation, aiming at establishing dense correspondences
between 2D pixels of human body and 3D human body template, is a key technique
in enabling machines to have an understanding of people in images. It still
poses several challenges due to practical scenarios where real-world scenes are
complex and only partial annotations are available, leading to incompelete or
false estimations. In this work, we present a novel framework to detect the
densepose of multiple people in an image. The proposed method, which we refer
to Knowledge Transfer Network (KTN), tackles two main problems: 1) how to
refine image representation for alleviating incomplete estimations, and 2) how
to reduce false estimation caused by the low-quality training labels (i.e.,
limited annotations and class-imbalance labels). Unlike existing works directly
propagating the pyramidal features of regions for densepose estimation, the KTN
uses a refinement of pyramidal representation, where it simultaneously
maintains feature resolution and suppresses background pixels, and this
strategy results in a substantial increase in accuracy. Moreover, the KTN
enhances the ability of 3D based body parsing with external knowledges, where
it casts 2D based body parsers trained from sufficient annotations as a 3D
based body parser through a structural body knowledge graph. In this way, it
significantly reduces the adverse effects caused by the low-quality
annotations. The effectiveness of KTN is demonstrated by its superior
performance to the state-of-the-art methods on DensePose-COCO dataset.
Extensive ablation studies and experimental results on representative tasks
(e.g., human body segmentation, human part segmentation and keypoints
detection) and two popular densepose estimation pipelines (i.e., RCNN and
fully-convolutional frameworks), further indicate the generalizability of the
proposed method.
