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arXiv · 2610.04006

Verifier-Guided Synthetic Augmentation for 3D Human Shape Generation

Yuexuan Wu·Yang Xiang·Hamid Laga·Dip Das·Anuj Srivastava·Zhengwu Zhang
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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.

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Yuexuan Wu, Yang Xiang, Hamid Laga, Dip Das, Anuj Srivastava, Zhengwu Zhang. 2026-10-02. Verifier-Guided Synthetic Augmentation for 3D Human Shape Generation. https://arxiv.org/abs/2610.04006

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