Aiming at the problem that the existing human pose transfer methods suffer from image deformation and distortion due to improper feature processing in the encoding stage, a multi-resolution human pose transfer method based on pose-attentional transfer network (PATN) and self-attention mechanism is proposed. Firstly, a pose-guided self-attention module is designed to enhance the weight of the feature channel of the key body region through the multi-head attention mechanism, reduce the influence of background-irrelevant features, and adaptively explore the correlation between the two branch features. Secondly, a multi-scale attention module is added in the decoding stage to enhance the expression of pose information at different scales, effectively improve the fidelity of local details and overall texture. Finally, the ternary pixel loss is introduced to constrain the generated image, which improves the feature consistency and structural consistency of the image. The experimental results are verified on the DeepFashion and Market-1501 datasets. They show that the proposed method is superior to the existing PATN method in terms of structural similarity (SSIM), initial score (IS) and perceptual similarity (LPIPS), and has improved visual perception and edge texture, showing important potential in the downstream task of person re-identification.