  Body segmentation is an important step in many computer vision problems
involving human images and one of the key components that affects the
performance of all downstream tasks. Several prior works have approached this
problem using a multi-task model that exploits correlations between different
tasks to improve segmentation performance. Based on the success of such
solutions, we present in this paper a novel multi-task model for human
segmentation/parsing that involves three tasks, i.e., (i) keypoint-based
skeleton estimation, (ii) dense pose prediction, and (iii) human-body
segmentation. The main idea behind the proposed Segmentation--Pose--DensePose
model (or SPD for short) is to learn a better segmentation model by sharing
knowledge across different, yet related tasks. SPD is based on a shared deep
neural network backbone that branches off into three task-specific model heads
and is learned using a multi-task optimization objective. The performance of
the model is analysed through rigorous experiments on the LIP and ATR datasets
and in comparison to a recent (state-of-the-art) multi-task body-segmentation
model. Comprehensive ablation studies are also presented. Our experimental
results show that the proposed multi-task (segmentation) model is highly
competitive and that the introduction of additional tasks contributes towards a
higher overall segmentation performance.
