4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in
  varying outfits exhibiting large displacements
Matthieu Armando Laurence Boissieux Edmond Boyer Jean-Sebastien Franco Martin Humenberger Christophe Legras Vincent Leroy Mathieu Marsot Julien Pansiot Sergi Pujades Rim Rekik Gregory Rogez Anilkumar Swamy Stefanie Wuhrer
This work presents 4DHumanOutfit, a new dataset of densely sampled
spatio-temporal 4D human motion data of different actors, outfits and motions.
The dataset is designed to contain different actors wearing different outfits
while performing different motions in each outfit. In this way, the dataset can
be seen as a cube of data containing 4D motion sequences along 3 axes with
identity, outfit and motion. This rich dataset has numerous potential
applications for the processing and creation of digital humans, e.g. augmented
reality, avatar creation and virtual try on. 4DHumanOutfit is released for
research purposes at https://kinovis.inria.fr/4dhumanoutfit/. In addition to
image data and 4D reconstructions, the dataset includes reference solutions for
each axis. We present independent baselines along each axis that demonstrate
the value of these reference solutions for evaluation tasks.