HPSv3: Towards Wide-Spectrum Human Preference Score
Authors: name: Yuhang Ma, name: Xiaoshi Wu, name: Keqiang Sun, name: Hongsheng Li

Evaluating text-to-image generation models requires alignment with human
perception, yet existing human-centric metrics are constrained by limited data
coverage, suboptimal feature extraction, and inefficient loss functions. To
address these challenges, we introduce Human Preference Score v3 (HPSv3). (1)
We release HPDv3, the first wide-spectrum human preference dataset integrating
1.08M text-image pairs and 1.17M annotated pairwise comparisons from
state-of-the-art generative models and low to high-quality real-world images.
(2) We introduce a VLM-based preference model trained using an
uncertainty-aware ranking loss for fine-grained ranking. Besides, we propose
Chain-of-Human-Preference (CoHP), an iterative image refinement method that
enhances quality without extra data, using HPSv3 to select the best image at
each step. Extensive experiments demonstrate that HPSv3 serves as a robust
metric for wide-spectrum image evaluation, and CoHP offers an efficient and
human-aligned approach to improve image generation quality. The code and
dataset are available at the HPSv3 Homepage.