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PSP-HDRI$+$: A Synthetic Dataset Generator for Pre-Training of Human-Centric Computer Vision Models

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arxiv 2207.05025 v1 pith:3HWGLWGO submitted 2022-07-11 cs.CV cs.AIcs.DBcs.GRcs.LG

classification cs.CVcs.AIcs.DBcs.GRcs.LG
keywords syntheticdatageneratormodelpre-trainingpsp-hdriablationalternative
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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

    cs.CV 2026-08 conditional novelty 6.0 of 10

    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.

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