JiHyuk-Byun/

3D-PAQA

Preference-aligned perceptual quality labels for 240,636 Objaverse assets, rated on six perceptual criteria. The goal of 3D-PAQA is to move beyond synthetic-distortion 3D-QA benchmarks and provide human-preference-aligned quality scores for real, human-created 3D assets, at a scale usable for training and benchmarking automatic quality evaluators. Drawn from a 264,966-asset Objaverse corpus.

train.csv — 216,540 labeled assets… See the full description on the dataset page: https://huggingface.co/datasets/JiHyuk-Byun/3D-PAQA.