Headset: Human emotion awareness under partial occlusions multimodal dataset
<|reference_start|>Headset: Human emotion awareness under partial occlusions multimodal dataset: The volumetric representation of human interactions is one of the fundamental domains in the development of immersive media productions and telecommunication applications. Particularly in the context of the rapid advancement of Extended Reality (XR) applications, this volumetric data has proven to be an essential technology for future XR elaboration. In this work, we present a new multimodal database to help advance the development of immersive technologies. Our proposed database provides ethically compliant and diverse volumetric data, in particular 27 participants displaying posed facial expressions and subtle body movements while speaking, plus 11 participants wearing head-mounted displays (HMDs). The recording system consists of a volumetric capture (VoCap) studio, including 31 synchronized modules with 62 RGB cameras and 31 depth cameras. In addition to textured meshes, point clouds, and multi-view RGB-D data, we use one Lytro Illum camera for providing light field (LF) data simultaneously. Finally, we also provide an evaluation of our dataset employment with regard to the tasks of facial expression classification, HMDs removal, and point cloud reconstruction. The dataset can be helpful in the evaluation and performance testing of various XR algorithms, including but not limited to facial expression recognition and reconstruction, facial reenactment, and volumetric video. HEADSET and its all associated raw data and license agreement will be publicly available for research purposes.<|reference_end|>
@article{lohesara2024headset:,
    title={Headset: Human emotion awareness under partial occlusions multimodal
  dataset},
    author={Fatemeh Ghorbani Lohesara, Davi Rabbouni Freitas, Christine Guillemot,
  Karen Eguiazarian, Sebastian Knorr},
    journal={arXiv preprint arXiv:2402.09107},
    year={2024},
    doi={10.1109/TVCG.2023.3320236},
    archivePrefix={arXiv},
    eprint={2402.09107},
    primaryClass={cs.CV}
}