针对目前人物图像生成模型普遍存在糊化和纹理缺失等问题,提出一种融合特征反馈机制的姿态引导人物图像生成模型,该模型采用生成式对抗神经网络进行训练,在姿势集成和图像细化阶段生成模型的基础上提出一种特征信息反馈机制,使得生成模型的每个阶段都会受到特征比对调节。受到迁移学习的启发,将在ImageNet数据集上预训练的权重作为模型特征层的初始权重,并在训练过程中进行相应微调,旨在增强图像生成模型的稳健性和鲁棒性,提高生成图像的质量。实验结果表明,所提模型能够获取较为真实细腻,符合人类视觉感知的人物图像。

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.