HPSv3: Towards Wide-Spectrum Human Preference Score
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.
6894142c741a16f544fbcec2
Yuhang Ma
6894142c741a16f544fbcec3
Xiaoshi Wu
6894142c741a16f544fbcec4
Keqiang Sun
6894142c741a16f544fbcec5
Hongsheng Li
HPSv3: Towards Wide-Spectrum Human Preference Score
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.
HPSv3, a human preference score using a wide-spectrum dataset and uncertainty-aware ranking loss, enhances text-to-image generation quality through iterative refinement.
human preference score HPDv3 text-image pairs pairwise comparisons VLM-based preference model uncertainty-aware ranking loss Chain-of-Human-Preference CoHP image refinement image generation quality