To address the limitations of character image generation models, such as ambiguity and lack of texture, this study proposes a pose-guided character image generation model incorporating a fusion feature feedback mechanism. Generative adversarial neural networks are used for training the proposed model. Further, the proposed model is generated during the postural integration and image refinement stages. A fusion feature information feedback mechanism is proposed based on the model to ensure that each stage of the generated model will be subjected to feature comparison adjustment. Inspired by transfer learning, the pre-trained weights of the ImageNet dataset are used as the initial weights of the model feature layer. Moreover, to enhance the robustness of the image generation model and improve the quality of the generated images, corresponding fine-tuning is performed during the training process. Experimental results reveal that the proposed model can obtain more realistic and delicate images of humans that are consistent with human visual perception.