Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Head portraits are popular in traditional painting. Automating portrait painting is challenging as the human visual system is sensitive to the slightest irregularities in human faces. Applying generic painting techniques often deforms facial structures. On the other hand portrait painting techniques are mainly designed for the graphite style and/or are based on image analogies; an example painting as ll as its original unpainted version are required. This limits their domain of applicability.  present a new technique for transferring the painting from a head portrait onto another. Unlike previous work our technique only requires the example painting and is not restricted to a specific style.  impose novel spatial constraints by locally transferring the color distributions of the example painting. This better captures the painting texture and maintains the integrity of facial structures.  generate a solution through Convolutional Neural Networks and  present an extension to video. Here motion is exploited in a way to reduce temporal inconsistencies and the shor-door effect. Our approach transfers the painting style while maintaining the input photograph identity. In addition it significantly reduces facial deformations over state of the art.
['Ahmed Selim', 'Mohamed A. Elgharib', 'Linda Doyle']
I am writing a research paper on a idea Painting style transfer for head portraits using convolutional neural networks
[{"from": "human", "value": "I am writing a research paper on a idea Painting style transfer for head portraits using convolutional neural networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Head portraits are popular in traditional painting. Automating portrait painting is challenging as the human visual system is sensitive to the slightest irregularities in human faces. Applying generic painting techniques often deforms facial structures. On the other hand portrait painting techniques are mainly designed for the graphite style and/or are based on image analogies; an example painting as ll as its original unpainted version are required. This limits their domain of applicability.  present a new technique for transferring the painting from a head portrait onto another. Unlike previous work our technique only requires the example painting and is not restricted to a specific style.  impose novel spatial constraints by locally transferring the color distributions of the example painting. This better captures the painting texture and maintains the integrity of facial structures.  generate a solution through Convolutional Neural Networks and  present an extension to video. Here motion is exploited in a way to reduce temporal inconsistencies and the shor-door effect. Our approach transfers the painting style while maintaining the input photograph identity. In addition it significantly reduces facial deformations over state of the art."}]