  We address the problem of learning a single model for person
re-identification, attribute classification, body part segmentation, and pose
estimation. With predictions for these tasks we gain a more holistic
understanding of persons, which is valuable for many applications. This is a
classical multi-task learning problem. However, no dataset exists that these
tasks could be jointly learned from. Hence several datasets need to be combined
during training, which in other contexts has often led to reduced performance
in the past. We extensively evaluate how the different task and datasets
influence each other and how different degrees of parameter sharing between the
tasks affect performance. Our final model matches or outperforms its
single-task counterparts without creating significant computational overhead,
rendering it highly interesting for resource-constrained scenarios such as
mobile robotics.
