Poplar-9K is a curated human-centric image dataset generated through a reproducible attribute sampling, diffusion rendering, and vision-language inspection pipeline, with 9,401 accepted pairs from 11,765 candidates.
PSP-HDRI$+$: A Synthetic Dataset Generator for Pre-Training of Human-Centric Computer Vision Models
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We introduce a new synthetic data generator PSP-HDRI$+$ that proves to be a superior pre-training alternative to ImageNet and other large-scale synthetic data counterparts. We demonstrate that pre-training with our synthetic data will yield a more general model that performs better than alternatives even when tested on out-of-distribution (OOD) sets. Furthermore, using ablation studies guided by person keypoint estimation metrics with an off-the-shelf model architecture, we show how to manipulate our synthetic data generator to further improve model performance.
citation-role summary
citation-polarity summary
fields
cs.CV 1years
2026 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Poplar: A Scalable Pipeline for Human-Centric Image Dataset Synthesis
Poplar-9K is a curated human-centric image dataset generated through a reproducible attribute sampling, diffusion rendering, and vision-language inspection pipeline, with 9,401 accepted pairs from 11,765 candidates.