To address the limitations of current virtual try-on methods in preserving local details of target garments adequately， and the problem that when diffusion model is used for generation， the Variational AutoEncoder （VAE）'s mapping of input data to low-dimensional space leads to loss of high-frequency detailed features in model’s hands and face， a human-centric detail-enhanced virtual try-on method was proposed. Firstly， the clothing-agnostic human body map， human pose map， and target garment were input into a Geometric Matching Module （GMM） to generate a coarsely warped garment result. Secondly， a Garment Wrap Refinement （GWR） module was constructed to enhance the detailed features of the coarsely warped garment. Thirdly， the warped garment map， clothing-agnostic human body map， and human pose map were concatenated and fed into a UNet with textual features， and textual and image features were fused to generate a clear image progressively through denoising. Fourthly， a Mask Feature Connection （MFC） module was constructed， and a coordinate attention was introduced， so as to localize the model’s position more accurately and preserve high-frequency detailed features in hands and face， thereby ensuring human-centric results. Finally， the output of MFC module and UNet were fused and decoded to obtain the final try-on results. Experimental results demonstrate that the proposed method achieves a 1.41% improvement in Structural Similarity Index Measure （SSIM） metric on the Dress Code dataset， along with reductions of 7.32%， 31.03%， and 64.56% in Learned Perceptual Image Patch Similarity （LPIPS）， FID （Fréchet Inception Distance）， and KID （Kernel Inception Distance） metrics， respectively， compared to the LADI-VTON （LAtent DIffusion-Virtual Try-ON） method， verifying that the proposed method achieves superior performance in virtual try-on.