Chehre: An Emoji-Prompted Dataset to Explore Perceptual Flexibility in Video Language Models
Do people perceive the same facial expression in the same way? Should we expect vision models to be flexible in how they perceive facial expressions? Facial expressions are nonverbal social signals used in human interaction, but facial expression recognition datasets often focus on a single deterministic annotation per sample. We introduce Chehre, an emoji-prompted video dataset with a wide range of dynamic facial expressions for exploring perceptual variation. In Chehre, 203 participants were prompted to express and record 40 facial emojis. Later, their facial motions were transferred onto synthetic faces to preserve privacy. A separate group annotated the videos, resulting in 2,111 vide...
| Authors | Bita Azari, Zoe Stanley, Avneet Batra et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 June 2026 |
| Last updated at source | 2 September 2026 |
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