{
  "abstract": "Limited training data diversity constrains generative modeling of 3D human bodies: conservative models remain close to observed examples, whereas exploratory models often violate basic body proportions. We introduce a verifier-guided augmentation framework that uses global and mode-local PCA to generate inexpensive candidates, screens them using correspondence-derived skeletal proportions and body-part geometry, and retrains a diffusion model on accepted candidates. Elastic registration provides both the modal structure used by distributed PCA and the dense anatomical correspondence needed for scalable screening without per-candidate body-model fitting. A blinded human study supports the verifier as a conservative gatekeeper, favoring verifier-accepted over rejected outputs. We evaluate full-pool verifier acceptance separately from the coverage and departure of accepted samples and combine them through EAUC. On 4,498 registered DFAUST surfaces, distributed-PCA augmentation achieves 86.32% acceptance, the highest CP-AUC (0.871), and the highest EAUC (0.752), improving EAUC by 32% over real-only and self-augmented diffusion. These results show that mode-local, verifier-guided proposals broaden diffusion generation while maintaining high agreement with calibrated body measurements.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.04006",
  "author_total": 6,
  "authors": [
    {
      "name": "Yuexuan Wu"
    },
    {
      "name": "Yang Xiang"
    },
    {
      "name": "Hamid Laga"
    },
    {
      "name": "Dip Das"
    },
    {
      "name": "Anuj Srivastava"
    },
    {
      "name": "Zhengwu Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cmvvv9qg48mxt.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.04006",
  "fulltext_url": null,
  "id": "cmvvv9qg48mxt",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.04006",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "2206|3D Shape Modeling and Analysis"
    ]
  ],
  "openalex": "W7220716025",
  "pdf_url": "https://arxiv.org/pdf/2610.04006",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cmvvv9qg48mxt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:16Z",
      "native_id": "2610.04006",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.04006"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Verifier-Guided Synthetic Augmentation for 3D Human Shape Generation",
  "title_text": "Verifier-Guided Synthetic Augmentation for 3D Human Shape Generation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:16Z",
  "url": "https://research.draconitic.com/paper/cmvvv9qg48mxt",
  "venue": "arXiv",
  "versions": []
}