Long-horizon Naturalistic Interaction
168 semi-structured interviews conducted by licensed counselors, eliciting authentic nonverbal cues across extended social interactions, not thin-slice impressions.
Understanding personality from visual behavior remains challenging. Existing benchmarks rely on short, crowd-annotated clips and capture only coarse impressions, lacking temporal continuity, clinical validity, and fine-grained sub-trait structure.
168 semi-structured interviews conducted by licensed counselors, eliciting authentic nonverbal cues across extended social interactions, not thin-slice impressions.
Continuous scores for all five Big Five domains and, for the first time in PTI benchmarks, fifteen validated BFI-2 sub-traits, scored independently by two psychology professionals.
A 5,000-clip research subset with expert BFI-2 annotations is now available on Hugging Face through individual application and manual approval.
PersoMoni bridges the gap between ecological validity and computational tractability, preserving the richness of full interviews while providing localized clip units for learning.
A distribution-aware subset of 5,000 processed facial clips is available now: 3,997 training clips, 1,003 test clips, 199,897 JPEG frames, and 20 BFI-2 targets. Access is granted to named researchers after application and manual review. The current release does not include the complete original interview videos. Full-dataset feature-map processing and quality control remain in progress.
| Participants | 168 (86 F / 82 M) |
|---|---|
| Video clips | 20,000+ face-aligned segments |
| Duration | ~25 min avg. per interview |
| Resolution | 1080p @ 30 fps |
| Label space | 20-dim continuous BFI-2 vector |
| Annotation | Dual expert rating + averaging |
| Demographics | Balanced age & education groups |
PersoMoni is the first personality computing benchmark to extend granularity from 5 to 15 dimensions, aligned with the psychometrically validated BFI-2 hierarchical framework.
If you use the PersoMoni dataset in your research, please cite our paper.
@article{cui2026persomoni,
author = {Cui, Feng-Qi and Huang, Jinyang and Zhao, Sirui and Li, Kun and Liu, Zhi and Li, Meng and Jia, Ziyu and Guo, Dan and Wang, Meng},
journal = {IEEE Transactions on Affective Computing},
title = {PersoMoni: A Comprehensive Video-Based Benchmark Dataset for Fine-grained Personality Assessment with 15 Trait Dimensions},
year = {2026},
pages = {1--14},
doi = {10.1109/TAFFC.2026.3698795},
url = {https://ieeexplore.ieee.org/document/11543185}
}