Beyond Holistic Realism: Dimension-wise Garment Fidelity Assessment for Virtual Try-On Evaluation and Optimization

Virtual try-on (VTON) demands not only plausible visual synthesis but, crucially, faithful preservation of reference garment characteristics. However, conventional evaluation metrics—such as PSNR, SSIM, KID, and FID—primarily capture superficial or global realism, failing to reliably reflect multi-dimensional garment consistency. To address this limitation, we introduce DAT, a Dimension-wise Assessment framework for virtual Try-on. DAT disentangles garment fidelity into seven interpretable and critical dimensions: silhouette, color, neckline and sleeve shape, major decoration and structure, material texture, fine-detail fidelity, and logo preservation, formulating each as a dedicated attribute-level prediction task. To effectively train this assessment model, we implement a two-stage learning paradigm comprising scalable weak supervision on 50K paired samples, followed by high-precision refinement on 10K curated annotations distilled via multi-model consensus. Furthermore, a dimensional-weighted cross-entropy loss is introduced to counter severe label imbalances inherent across evaluation categories. Beyond benchmarking, we establish a closed-loop optimization by seamlessly integrating DAT into the reinforcement learning pipeline of Qwen-Image-Edit for VTON, where dimension-wise rewards are adaptively aggregated to prioritize under-optimized visual attributes. Extensive experiments demonstrate that our 8B model achieves state-of-the-art performance across balanced accuracy, SROCC, and PLCC—surpassing prominent frontier proprietary models including Gemini-3.1, Qwen3.7-plus, and GPT-5.5—while serving as an exceptionally effective supervision signal for reward-guided garment generation.