Recent advances in diffusion models have significantly elevated the visual fidelity of Virtual Try-On (VTON) systems, yet reliable evaluation remains a persistent bottleneck. Traditional metrics struggle to quantify fine-grained texture details and semantic consistency, while existing datasets fail to meet commercial standards in scale and diversity. We present OpenVTON-Bench, a large-scale benchmark comprising approximately 100K high-resolution image pairs (up to $1536 \times 1536$). The dataset is constructed using DINOv3-based hierarchical clustering for semantically balanced sampling and Gemini-powered dense captioning, ensuring a uniform distribution across 20 fine-grained garment categories. To support reliable evaluation, we propose a multi-modal protocol that measures VTON quality along five interpretable dimensions: background consistency, identity fidelity, texture fidelity, shape plausibility, and overall realism. The protocol integrates VLM-based semantic reasoning with a novel Multi-Scale Representation Metric based on SAM3 segmentation and morphological erosion, enabling the separation of boundary alignment errors from internal texture artifacts. Experimental results show strong agreement with human judgments (Kendall's $τ$ of 0.833 vs. 0.611 for SSIM), establishing a robust benchmark for VTON evaluation.

翻译：近期扩散模型的进展显著提升了虚拟试穿系统的视觉保真度，但可靠评估仍是一个持续存在的瓶颈。传统指标难以量化细粒度纹理细节与语义一致性，而现有数据集在规模和多样性方面无法满足商业标准。本文提出OpenVTON-Bench，一个包含约10万对高分辨率图像（最高$1536 \times 1536$）的大规模基准数据集。该数据集基于DINOv3层次聚类进行语义均衡采样，并采用Gemini驱动的密集描述生成技术，确保20个细粒度服装类别的均匀分布。为支撑可靠评估，我们提出一种多模态评估协议，从背景一致性、身份保真度、纹理保真度、形状合理性和整体真实感五个可解释维度衡量虚拟试穿质量。该协议融合基于视觉语言模型的语义推理与新型多尺度表征指标（基于SAM3分割与形态学腐蚀），可实现边界对齐误差与内部纹理伪影的分离。实验结果表明，该指标与人类判断高度一致（Kendall $τ$系数0.833，对比SSIM的0.611），为虚拟试穿评估建立了稳健基准。