4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements
This dataset addresses the need for multi-subject 4D human motion data with outfit variations for researchers in digital human processing, though it is incremental as it builds on existing data collection efforts.
The authors introduced 4DHumanOutfit, a dataset of 4D human motion sequences with variations in actors, outfits, and motions, designed for applications like augmented reality and avatar creation, and they provided baselines to demonstrate its utility for evaluation.
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.