构建可扩展的人像图像数据集合成流水线，提升真实感与多样性。
Poplar: A Scalable Pipeline for Human-Centric Image Dataset Synthesis
- 分三步：指定属性约束、生成图像、结构化审查，确保质量可控。
- 从11,765张图中筛选出9,401对高质量人像图文数据，保留率79.9%。
- 适合需要定制化人像数据集的研究者，支持可复现与审计。
当前图像生成器虽能合成逼真的人像图像，但构建可用的数据集仍远超单图生成。人像数据集需覆盖多样化人物与场景，避免不合理属性组合，保持日常摄影风格，并实现大规模质量控制。我们提出Poplar，一个可复现的Specify--Render--Inspect流水线，用于人像图像数据集合成。首先在常识约束下指定结构化属性，并转化为面向摄影的提示词；其次使用适应真实感的图像生成器，在感知构图的长宽比下生成图像，并重试明显技术失败；最后通过统一的结构化视觉-语言审查流程评估每张候选图，保留原始提示的同时剔除内在缺陷或提示不符内容。基于Poplar，我们构建了Poplar-9K：从11,765个候选样本中精选出9,401对经审核的人像图文对（接受率79.9%）。我们公开发布该数据集及流水线、配置文件、不可变生成提示和可审计的审查记录，形成一套紧凑、可定制的人像数据集构建资源。
原文摘要 · Abstract (English)
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.
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