Anatomically Plausible Human Image Generation via Synthetic Localized Preferences

Large-scale text-to-image foundation models have substantially improved the visual realism of generated human images. However, their outputs may still exhibit anatomical artifacts in structurally complex regions. Preference-based alignment offers an architecture-preserving approach to improving anatomical plausibility. In practice, conventional preference pairs typically compare independently generated images whose differences extend beyond anatomy, making it unclear whether anatomical quality drives the preference. To provide direct anatomical supervision, we present Alignment via Synthetic Anatomical Preferences (ASAP), a data-centric framework built on synthetic localized preferences. ASAP constructs synthetic localized preferences by using verified anatomically plausible images as preferred anchors and creating dispreferred images via localized degradation, while largely preserving prompt semantics and non-target visual content. This pipeline yields the Human Anatomical Preference (HAP) dataset, comprising over 10K curated pairs with region masks and anatomical error metadata. To exploit their spatial attribution while preserving overall image quality, we adapt Direct Preference Optimization (DPO) with a region-aware preference gap that emphasizes modified regions and a margin-bounded regression objective that drives the gap toward a finite target. We further introduce HAF-Bench, a benchmark for systematic evaluation of anatomical fidelity across diverse scenarios. Extensive experiments across diffusion and flow-matching backbones demonstrate that ASAP consistently reduces anatomical errors while maintaining overall image quality.