Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  focuses on the problem of generating human face pictures from specific attributes. The existing CNN-based face generation models, hover, either ignore the identity of the generated face or fail to preserve the identity of the reference face image. Here  address this problem from the view of optimization, and suggest an optimization model to generate human face with the given attributes while keeping the identity of the reference image. The attributes can be obtained from the attribute-guided image or by tuning the attribute features of the reference image. With the deep convolutional network "VGG-Face", the loss is defined on the convolutional feature maps.  then apply the gradient decent algorithm to solve this optimization problem. The results validate the effectiveness of our method for attribute driven and identity-preserving face generation.
['Mu Li', 'Wangmeng Zuo', 'David Zhang']
I am writing a research paper on a idea Convolutional Network for Attribute-driven and Identity-preserving Human Face Generation
[{"from": "human", "value": "I am writing a research paper on a idea Convolutional Network for Attribute-driven and Identity-preserving Human Face Generation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  focuses on the problem of generating human face pictures from specific attributes. The existing CNN-based face generation models, hover, either ignore the identity of the generated face or fail to preserve the identity of the reference face image. Here  address this problem from the view of optimization, and suggest an optimization model to generate human face with the given attributes while keeping the identity of the reference image. The attributes can be obtained from the attribute-guided image or by tuning the attribute features of the reference image. With the deep convolutional network \"VGG-Face\", the loss is defined on the convolutional feature maps.  then apply the gradient decent algorithm to solve this optimization problem. The results validate the effectiveness of our method for attribute driven and identity-preserving face generation."}]