| title | Poplar: A Scalable Pipeline for Human-Centric Image Dataset Synthesis |
|---|---|
| authors | Zhishan Zou |
| date | 2026-08-01 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.00440 |
| upvotes | 6 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Recent image generators can synthesize convincing human-centric images, yet producing a useful collection remains different from producing a single successful image. A human-centric dataset must cover varied people and contexts, avoid implausible attribute combinations, preserve an everyday photographic character, and expose quality-control decisions at scale. We present Poplar, a reproducible Specify--Render--Inspect pipeline for human-centric image dataset synthesis. Specify samples structured attributes under commonsense constraints and verbalizes them as photography-oriented prompts. Render uses a realism-adapted image generator across composition-aware aspect ratios and retries obvious technical failures. Inspect applies a single structured vision--language review to each candidate, preserving the original prompt while rejecting intrinsic image defects or material prompt mismatches. Using Poplar, we construct Poplar-9K: 9,401 curated human-centric image--text pairs retained from 11,765 reviewed candidates (79.9\% acceptance). We release the dataset together with the pipeline, configurations, immutable generation prompts, and auditable inspection records as a compact resource for building customizable human-centric collections. |
cite
@misc{zou2026poplar,
title={Poplar: A Scalable Pipeline for Human-Centric Image Dataset Synthesis},
author={Zhishan Zou},
year={2026},
eprint={2608.00440},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.00440}
}