4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Computer Vision and Image Understanding Année : 2023

4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements

Matthieu Armando
Edmond Boyer
Vincent Leroy
Mathieu Marsot
Sergi Pujades
Rim Rekik Dit Nekhili
Grégory Rogez
  • Fonction : Auteur
  • PersonId : 1006026
Stefanie Wuhrer

Résumé

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.
Fichier principal
Vignette du fichier
4DHumanOutfit-HAL.pdf (24.1 Mo) Télécharger le fichier
Vignette du fichier
DataCube_teaser.png (2.17 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Format : Figure, Image

Dates et versions

hal-04129186 , version 1 (15-06-2023)

Licence

Paternité

Identifiants

Citer

Matthieu Armando, Laurence Boissieux, Edmond Boyer, Jean-Sébastien Franco, Martin Humenberger, et al.. 4DHumanOutfit: a multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements. Computer Vision and Image Understanding, 2023, 237, ⟨10.1016/j.cviu.2023.103836⟩. ⟨hal-04129186⟩
127 Consultations
48 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More