Attractive Faces Skew Text-to-Image Reward Models and Leaderboards
We identify a key observation: for human faces, perceived attractiveness overrides image quality in preference judgments, even when comparing real photographs of identical fidelity.
ProofPaper ↗
Key points
- Human preference is now central to the evaluation, guidance, finetuning, and ranking of text-to-image models.
- However, the susceptibility of human preferences to bias in image generation remains underexplored.
- In a controlled user study, annotators rank more attractive faces as higher quality, even when unattractive real faces are compared with attractive AI-generated ones.
- Editing 40 annotated facial attributes in isolation, we find that attractiveness is the strongest correlate of score for four of five human preference models (PickScore, MPS, HPSv2.1, HPSv3), and the strongest predictor of score in regression analysis.
Sources (1)
- [1]Attractive Faces Skew Text-to-Image Reward Models and LeaderboardsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 01:35 PM
We identify a key observation: for human faces, perceived attractiveness overrides image quality in preference judgments, even when comparing real photographs of identical fidelity.
Human preference is now central to the evaluation, guidance, finetuning, and ranking of text-to-image models.
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