Poplar: A Scalable Pipeline for Human-Centric Image Dataset Synthesis
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 obvio...
| Authors | Zhishan Zou |
|---|---|
| Primary category | cs.CV |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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- choucsan/Poplar-9KDataset · cited by
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- choucsan/Poplar-9KDataset
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