OpenVTON-Bench: New Large-Scale Benchmark for Evaluating Virtual Try-On Systems
Researchers have released OpenVTON-Bench, a large-scale benchmark of approximately 100,000 high-resolution image pairs designed to evaluate AI-powered virtual try-on systems. The benchmark addresses longstanding gaps in evaluation reliability, including the inability of traditional metrics to capture fine-grained texture and semantic consistency. It establishes a more rigorous standard for assessing commercial-grade virtual try-on technology, with human-judgment agreement significantly outperforming existing metrics.
OpenVTON-Bench is a new benchmark dataset comprising roughly 100,000 high-resolution image pairs at resolutions up to 1536×1536 pixels, targeting the evaluation of virtual try-on (VTON) AI systems powered by diffusion models. The dataset was constructed using DINOv3-based hierarchical clustering for semantically balanced sampling and Gemini-powered dense captioning, ensuring uniform coverage across 20 fine-grained garment categories. To address the shortcomings of traditional image quality metrics, the authors propose a multi-modal evaluation protocol that assesses VTON outputs along five dimensions: background consistency, identity fidelity, texture fidelity, shape plausibility, and overall realism. A novel Multi-Scale Representation Metric, built on SAM3 segmentation and morphological erosion, enables the system to distinguish boundary alignment errors from internal texture artifacts. Experimental results show the proposed protocol achieves a Kendall's τ correlation of 0.833 with human judgments, compared to 0.611 for the widely used SSIM metric, indicating substantially stronger alignment with human perception. The paper is currently under review for the NeurIPS 2026 Datasets and Benchmarks Track.
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- arXiv cs.AICenter
OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation
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