Animation Head Sculpture Generation Algorithm Based on Improved Generative Adversarial Networks
In view of the problems of training instability,poor diversity of generated samples,poor effect on local details of characters and low quality of samples generated in most of the Generative Adversarial Networks on generation of the animation head sculptures,this paper constructs a distance penalty generator target function by using conditional entropy,and an improved model MGAN-ED is proposed combined with Attention Mechanism.The model mainly includes a generator integrated with multi-scale attention feature extraction unit and a multi-scale discriminator.The GAM and FID are used to evaluate the model.The experimental results show that the model can effectively solve the problem of pattern collapse,and the local details of the generated image are clearer and the quality of the generated samples is higher.
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