  Automatically producing instructions to modify one's posture could open the
door to endless applications, such as personalized coaching and in-home
physical therapy. Tackling the reverse problem (i.e., refining a 3D pose based
on some natural language feedback) could help for assisted 3D character
animation or robot teaching, for instance. Although a few recent works explore
the connections between natural language and 3D human pose, none focus on
describing 3D body pose differences. In this paper, we tackle the problem of
correcting 3D human poses with natural language. To this end, we introduce the
PoseFix dataset, which consists of several thousand paired 3D poses and their
corresponding text feedback, that describe how the source pose needs to be
modified to obtain the target pose. We demonstrate the potential of this
dataset on two tasks: (1) text-based pose editing, that aims at generating
corrected 3D body poses given a query pose and a text modifier; and (2)
correctional text generation, where instructions are generated based on the
differences between two body poses.
