Poplar: A Scalable Pipeline for Human-Centric Image Dataset Synthesis

Hugging Face Daily Papers Papers
dataset-synthesis human-centric image-generation pipeline vision-language data-curation

Summary

Introduces Poplar, a scalable Specify-Render-Inspect pipeline for synthesizing human-centric image datasets, and releases Poplar-9K, a curated dataset of 9,401 image-text pairs with auditable inspection records.

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.
Original Article
View Cached Full Text

Cached at: 08/04/26, 05:37 AM

Paper page - Poplar: A Scalable Pipeline for Human-Centric Image Dataset Synthesis

Source: https://huggingface.co/papers/2608.00440

Abstract

Recentimagegeneratorscansynthesizeconvincinghuman-centricimages,yetproducingausefulcollectionremainsdifferentfromproducingasinglesuccessfulimage.Ahuman-centricdatasetmustcovervariedpeopleandcontexts,avoidimplausibleattributecombinations,preserveaneverydayphotographiccharacter,andexposequality-controldecisionsatscale.WepresentPoplar,areproducibleSpecify--Render--Inspectpipelineforhuman-centricimagedatasetsynthesis.Specifysamplesstructuredattributesundercommonsenseconstraintsandverbalizesthemasphotography-orientedprompts.Renderusesarealism-adaptedimagegeneratoracrosscomposition-awareaspectratiosandretriesobvioustechnicalfailures.Inspectappliesasinglestructuredvision--languagereviewtoeachcandidate,preservingtheoriginalpromptwhilerejectingintrinsicimagedefectsormaterialpromptmismatches.UsingPoplar,weconstructPoplar-9K:9,401curatedhuman-centricimage--textpairsretainedfrom11,765reviewedcandidates(79.9\%acceptance).Wereleasethedatasettogetherwiththepipeline,configurations,immutablegenerationprompts,andauditableinspectionrecordsasacompactresourceforbuildingcustomizablehuman-centriccollections.

View arXiv pageView PDFProject pageGitHub5Add to collection

Get this paper in your agent:

hf papers read 2608\.00440

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2608.00440 in a model README.md to link it from this page.

Datasets citing this paper1

#### choucsan/Poplar-9K Viewer• Updatedabout 2 hours ago • 9.4k • 204 • 3

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2608.00440 in a Space README.md to link it from this page.

Collections including this paper1

Similar Articles

SGOCR: A Spatially-Grounded OCR-focused Pipeline & V1 Dataset [P]

Reddit r/MachineLearning

SGOCR is an open-source dataset pipeline for generating spatially-grounded, OCR-focused visual question answering (VQA) tuples with rich metadata to support diverse VLM training. The pipeline uses a multi-stage approach combining models like Nvidia's nemotron-ocr-v2, Gemma4, Qwen3-VL, and Gemini-2.5-Flash, along with an agentic optimization loop.