  Several computer vision applications such as person search or online fashion
rely on human description. The use of instance-level human parsing (HP) is
therefore relevant since it localizes semantic attributes and body parts within
a person. But how to characterize these attributes? To our knowledge, only some
single-HP datasets describe attributes with some color, size and/or pattern
characteristics. There is a lack of dataset for multi-HP in the wild with such
characteristics. In this article, we propose the dataset CCIHP based on the
multi-HP dataset CIHP, with 20 new labels covering these 3 kinds of
characteristics. In addition, we propose HPTR, a new bottom-up multi-task
method based on transformers as a fast and scalable baseline. It is the fastest
method of multi-HP state of the art while having precision comparable to the
most precise bottom-up method. We hope this will encourage research for fast
and accurate methods of precise human descriptions.
