Go to ICLR 2026 Conference homepageVTBench: Comprehensive Benchmark Suite Towards Real-World Virtual Try-on Models
Keywords: Virtual Try-on, Benchmark
Abstract: While virtual try-on has achieved significant progress, evaluating these models towards real-world scenarios remains a challenge. A comprehensive benchmark is essential for three key reasons: (1) Current metrics inadequately reflect human perception, particularly in unpaired try-on settings; (2) Most existing test sets are limited to indoor scenarios, lacking complexity for real-world evaluation; and (3) An ideal system should guide future advancements in virtual try-on generation.
To address these needs, we introduce the **V**irtual **T**ry-on **Bench**mark (**VTBench**), the first-ever hierarchical try-on benchmark suite that systematically decomposes virtual image try-on into hierarchical, disentangled dimensions, each equipped with tailored test sets and evaluation criteria. VTBench exhibits three key advantages: 1) Multi-Dimensional Evaluation Framework: The benchmark encompasses five critical dimensions for virtual try-on generation (*e.g.,* overall image quality, texture preservation, complex background consistency, cross-category size adaptability, and hand-occlusion handling). Granular evaluation metrics of corresponding test sets pinpoint model capabilities and limitations across diverse, challenging scenarios. 2) Human Alignment: Human preference annotations are provided for each test set, ensuring the benchmark’s alignment with perceptual quality across all evaluation dimensions. 3) Valuable Insights: Beyond standard indoor settings, we analyze model performance variations across dimensions and investigate the disparity between indoor and real-world try-on scenarios. To foster the field of virtual try-on towards challenging real-world scenarios, VTBench will be open-sourced, including all test sets, evaluation protocols, generated results, and human annotations.
Primary Area: datasets and benchmarks
Submission Number: 4345
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